This thesis has been submitted in fulfilment of the requirements for a postgraduate degree

(e.g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following

terms and conditions of use:

• This work is protected by copyright and other intellectual property rights, which are

retained by the thesis author, unless otherwise stated.

• A copy can be downloaded for personal non-commercial research or study, without

prior permission or charge.

• This thesis cannot be reproduced or quoted extensively from without first obtaining

permission in writing from the author.

• The content must not be changed in any way or sold commercially in any format or

medium without the formal permission of the author.

• When referring to this work, full bibliographic details including the author, title,

awarding institution and date of the thesis must be given.

Cognitive abilities, personality and interests:

Their interrelations and impact on occupation

Jason Timothy Major

Doctor of Philosophy in Psychology

The University of Edinburgh

2013

ii

Declaration

I hereby declare that this work has been composed by me, and that it is my own

work, except where it has been clearly indicated. Furthermore, the work has not

been submitted for any other degree or professional qualification.

Jason Major

iii

Acknowledgements

I would like to thank my supervisors, Dr. Wendy Johnson and Professor Ian Deary,

for their great expertise and encouragement. During the PhD I benefited from an

intellectually lively department, particularly in the area of Individual Differences. I

would like to thank all those students and staff who contributed to it. I also would

like to thank my parents and family for their support.

iv

Abstract

Cognitive ability, personality and interests are three distinct topics of investigation

for psychology. In the past two decades, however, there have been growing appeals

for research and theories that address the overlap among these domains (Ackerman

& Heggestad, 1997; Armstrong, Day, McVay, & Rounds, 2008). One example of

such a theory is PPIK theory (intelligence-as-process, personality, interests, and

intelligence-as-knowledge) by Ackerman (1996). Integrative theories have the

potential of not only increasing our theoretical understanding of the development of

these individual differences, but of and improving vocational guidance through better

prediction of future occupation (Armstrong, Su, & Rounds, 2011; Johnson &

Bouchard, 2009). The research of this thesis was centered on examining the links

among cognitive ability, personality and interests. The data came from Project

TALENT (PT), a nationally-representative sample of approximately 400,000

American high school students from 1960 (Flanagan et al., 1962). A secondary topic

was whether an integrated view could improve the prediction of attained occupation.

This was tested with occupational data from follow-up PT surveys, conducted 11

years after high school. The first study addressed the structure of the PT intelligence

tests. Three popular models of intelligence were compared through factor analysis:

the Extended Fluid-Crystallized (Gf-Gc), Cattell-Horn-Carroll (CHC) and Verbal-

Perceptual-Image Rotation (VPR) models. The VPR model provided the best fit to

the data. The second study was an investigation of linear and nonlinear intelligence-

personality associations in Project TALENT. The ten PT personality scales were

related to the Big Five personality factors through content examination, consistent

with previous research (Reeve, Meyer, & Bonaccio, 2006). Through literature

review of studies on intelligence and the Big Five, 17 hypotheses were made about

linear associations and quadratic associations of personality traits with general

intelligence (g). The majority of the hypotheses were supported in all four grade

samples: 53% in male samples, and 58% in female samples. The most notable

finding, contrary to previous research, was that quadratic associations explained

substantive variance above and beyond linear effects for Sociability, Maturity, Vigor

and Leadership in males, and Sociability, Maturity and Tidiness in females. The

third study examined associations between cognitive ability and interests, and their

v

capacity to predict occupational type. Specifically, Ackerman’s PPIK theory

suggests that there are two “trait complexes” that are combinations of cognitive

abilities and interests (termed science/math and intellectual/cultural). Trait

complexes were derived from PT data separately by latent class analysis and factor

analysis. It was hypothesized that they should have validity equal to or greater than

individual intelligence and interests scores in predicting attained occupation.

Instead, trait complexes derived through latent class analysis predicted substantially

less variance in occupation than individual scales. The factor-analytic trait

complexes performed more like the scales, but one trait complex (which involved g

centrally) was inconsistent with PPIK theory. Overall, the trait complexes of PPIK

theory were not supported. The results of the three studies are discussed in the

context of existing integrative theories, and suggestions for future research are

provided.

vi

Publications from this thesis

Major, J. T., Johnson, W., & Deary, I. J. (2012). Comparing models of intelligence

in Project TALENT: The VPR model fits better than the CHC and Extended Gf-Gc

models. Intelligence, 40(6), 543-559.

Major, J. T., Johnson, W., & Deary, I. J. (in press). Linear and nonlinear relations

between personality and general intelligence in Project TALENT. Journal of

Personality and Social Psychology.

Major, J. T., Johnson, W., & Deary, I. J. (2013). Trait complexes of cognitive

abilities and interests and their relations to realized occupation. Manuscript in

preparation.

vii

Table of contents (text)

Acknowledgements .................................................................................................... iii

Abstract ...................................................................................................................... iv

Publications from this thesis .................................................................................... vi

Table of contents ...................................................................................................... vii

Chapter 1: Introduction ............................................................................................ 1

1.1 Theories of intelligence, personality and interests ............................................ 2

1.1.1 Intelligence .................................................................................................. 2

1.1.2 Personality ................................................................................................... 4

1.1.3 Occupational interests .................................................................................. 6

1.3 Integrative theories ............................................................................................ 7

1.3 Prediction of occupational type ....................................................................... 12

Chapter 2: Project TALENT’s design and measures ........................................... 14

2.1 Intelligence tests ............................................................................................... 15

2.2 Personality tests ................................................................................................ 16

2.3 Occupational interest tests ................................................................................ 17

2.4 Occupation at follow-up ................................................................................... 18

Chapter 3: Comparing the VPR, CHC and Extended Gf-Gc models ................. 20

3.1 Introduction ...................................................................................................... 20

3.1.1 Previous factor-analytic research on Project TALENT ............................. 26

3.2 Methods ............................................................................................................ 29

3.2.1 Sample ....................................................................................................... 29

3.2.2 Measures .................................................................................................... 29

3.2.3 Data preparation ......................................................................................... 32

3.2.4 Analysis method ........................................................................................ 34

3.3 Results .............................................................................................................. 35

3.3.1 Exploratory factor analysis ........................................................................ 35

3.3.2 Confirmatory factor analyses ..................................................................... 41

3.3 Discussion ........................................................................................................ 52

3.3.1 The three theories in Project TALENT ..................................................... 53

3.3.2 Variations in VPR model specifications .................................................... 56

3.3.3 Theoretical implications for the structure of intelligence .......................... 57

3.4 Linking cognitive ability with personality ....................................................... 60

Chapter 4: Linear and nonlinear associations between general intelligence and

personality ................................................................................................................. 62

4.1 Introduction ...................................................................................................... 62

4.1.2 Linear personality-intelligence associations in Project TALENT ............. 68

4.1.3 Possible nonlinear associations .................................................................. 69

viii

4.2 Method .............................................................................................................. 71

4.2.1 Sample ....................................................................................................... 71

4.2.2 Intelligence measures ................................................................................. 71

4.2.3 Personality measures ................................................................................. 72

4.2.4 The general factor of personality ............................................................... 72

4.2.5 Methods of analysis ................................................................................... 77

4.3 Results ........................................................................................................... 78

4.3.1 LMS results compared to GAM results ..................................................... 81

4.3.2 Grade and sex differences .......................................................................... 82

4.3.3 Figures 4.1 to 4.3: titles and captions ........................................................ 82

4.4 Discussion ........................................................................................................ 87

4.4.1 Conclusions and future directions ............................................................. 94

4.5 Integrating cognitive abilities and interests ...................................................... 95

Chapter 5: Trait complexes of cognitive abilities and interests and their

predictive validity for occupation ........................................................................... 96

5.1 Introduction ...................................................................................................... 96

5.1.2 Previous Project TALENT research ........................................................ 101

5.2 Method ............................................................................................................ 103

5.2.1 Sample ..................................................................................................... 103

5.2.2 Intelligence measures ............................................................................... 104

5.2.3 Interest measures ..................................................................................... 105

5.2.4 Occupational categories ........................................................................... 106

5.2.5 Method of analysis ................................................................................... 106

5.2.6 Interest and cognitive ability factors ........................................................ 107

5.3 Results ............................................................................................................ 111

5.3.1 Factor-analytic trait complexes ................................................................ 111

5.3.2 Latent class trait complexes ..................................................................... 116

5.3.3 Prediction of occupational type ............................................................... 118

5.3.4 Multinomial prediction ............................................................................ 124

5.4 Discussion ...................................................................................................... 125

Chapter 6: Conclusion ........................................................................................... 134

5.1. Cognitive ability ............................................................................................ 134

5.2. Personality-intelligence associations ............................................................. 134

5.3. Trait complexes ............................................................................................. 137

5.4. Suggestions for future research ..................................................................... 140

References ............................................................................................................... 142

Appendix A ............................................................................................................. 153

Appendix B ............................................................................................................. 155

Appendix C ............................................................................................................. 158

ix

Table of contents (Figures and Tables)

Figure 1.1 Holland’s interest hexagon 6

Table 3.1 Primary features of the CHC, Extended Gf-Gc and VPR models 22

Table 3.2 Project Talent test names, short descriptions, and reliabilities for

males/females 31

Table 3.3 Factor pattern matrices for grade 10 males/females in the broad selection

of PT tests 37

Table 3.4 Factor pattern matrices for grade 10 males/females in the narrow selection

of PT tests 40

Table 3.5 Factor correlation matrices for grade 10 males (below diagonal) and females

(above diagonal) in the broad and narrow selections of PT tests 41

Table 3.6 First-order loadings for the CHC, Extended Gf-Gc and VPR models in

the broad selection (grade 10 males) 43

Figure 3.1 Measurement model of the VPR model with factor loadings from

the grade 10 male sample 45

Figure 3.2 Measurement model of the Extended Fluid-Crystallized model with factor

loadings from the grade 10 male sample 46

Figure 3.3 Measurement model of the Cattell-Horn-Carroll model with factor loadings

from the grade 10 male sample 46

Table 3.7 Fit statistics of confirmatory factor models for the broad selection of PT tests 49

Table 3.8 First-order loadings for the CHC, Extended Gf-Gc and VPR models

in the narrow selection (grade 10 males) 51

Table 3.9 Fit statistics of confirmatory factor models for the narrow selection

of PT tests 52

Table 4.1 Associations of the Project TALENT personality scales with the Big Five 65

Table 4.2 Personality test descriptives 72

Table 4.3 Correlations among personality scales after removal of the general personality

factor (frade 10 males/females) 76

Table 4.4 Standardized linear and quadratic effects of g on the personality scales (males) 79

Table 4.5 Standardized linear and quadratic effects of g on the personality scales (fem.) 80

Figure 4.1 Mean personality as predicted by general intelligence (grade 10 males) 84

Figure 4.2 Mean personality as predicted by general intelligence (grade 10 females) 85

Figure 4.3 LMS and GAM-predicted sociability as a function of general intelligence

(grade 10 males) 86

Table 5.1 Occupation categories and sample percentages (grade 12 sample) 106

Table 5.2 Factor loadings for grade 12 males/females in the confirmatory

intelligence model 110

x

Table 5.3 Correlations matrix for interest composites and cognitive ability factors

(grade 12 males/females) 112

Table 5.4 CFA solution of interests and abilities (grade 12 males) 114

Table 5.5 CFA solution of interests and abilities (grade 12 females) 115

Table 5.6 Latent class means from LCA (grade 12 males) 117

Table 5.7 Latent class means from LCA (grade 12 females) 118

Table 5.8 Odds ratios of abilities and interests predicting job categories

(grade 12 males) 120

Table 5.9 Odds ratios of abilities and interests predicting job categories

(grade 12 females) 121

Table 5.10 Odds ratios of latent classes in predicting job category (grade 12 males) 123

Table 5.11 Odds ratios of latent classes in predicting job category (grade 12 fem.) 123

Table 5.12 Original sample composition and correct classification percentages for

multinomial regression (grade 12 males) 125

Table 5.13 Original sample composition and correct classification percentages for

multinomial regression (grade 12 females) 125

1

Chapter 1: Introduction

The two main domains of study in differential psychology are intelligence

and personality. More recently, occupational interests have received increased

attention, driven by the practical goal of improving vocational guidance. These three

domains of individual differences are not entirely independent, but overlap

(Ackerman & Heggestad, 1997; Barrick, Mount, & Gupta, 2003). This overlap has

led researchers to call for an integrative theory of individual differences that takes

intelligence, personality and interests into account (Ackerman & Heggestad, 1997;

Armstrong et al., 2008).

The overall purpose of this thesis was to explore the relations among the

intelligence, interests and personality domains. The secondary goal was to examine

the potential for an integrated view to improve the prediction of attained occupation.

In this introductory chapter, a review is provided of prominent theoretical models in

the three domains. Research on their integration is then reviewed, and finally the

ability of integrative theories to improve our understanding of occupational

attainment.

The data used in this thesis came from Project TALENT (PT), a longitudinal and

nationally-representative study of the aptitudes, interests, and backgrounds of

American high school students, started in 1960. Chapter 1 provides background on

how PT was conducted and the measures within it. Chapter 2 is a study that

examined the structure of the PT intelligence tests, comparing three of the

predominant intelligence models in the literature. Chapter 3 is a study that examined

linear and non-linear associations between personality and intelligence. Chapter 4 is

a study that focused on the associations between intelligence and occupational

interests. It examined the character of potential “trait complexes” of intelligence and

interests in the PT scales, and whether these trait complexes are better or worse

predictors of future occupational type than individual scores for cognitive ability and

interests. In chapter 5, a summary is given of what these studies have revealed about

the intersection of intelligence, personality and interests, and the potential of

2

integrative frameworks both to describe this overlap and be useful in the prediction

of occupation.

1.1 Theories of intelligence, personality and interests

In order to study individual difference variables, psychologists need not only

valid and reliable measures, but for multifaceted traits such as intelligence,

personality and interests, theoretical models of their makeup. In this section, a brief

but up-to-date picture is provided of the main theories for the three domains.

1.1.1 Intelligence

Research on the structure of intelligence has continued uninterrupted since

the early twentieth century, when Spearman (1904) first proposed the concept of

general intelligence (g), as the common factor underlying all cognitive ability tests.

In the past several decades, research has converged on the hierarchical model as the

best representation of cognitive abilities (Hunt, 2011; Reeve & Bonaccio, 2011).

Here the term ‘hierarchical’ is used in the sense of a multiple-strata model, in which

higher-order or more general cognitive ability factors are proposed to contribute

directly to the lower-order or more specific ability factors. The lowest factor stratum

consists of narrow abilities measured by individual tests. The second stratum

consists of broad ability factors that emerge from higher-order factor analysis of

narrow abilities. The third stratum emerges from factor analysis of broad abilities,

but at this level only a single factor, known as g, is typically found.

Hierarchical models have both advantages and disadvantages in describing

intelligence. One advantage is that the highly general concept of intelligence is

divided into more manageable components called cognitive abilities (Reeve &

Bonaccio, 2011). A cognitive ability can be defined as a latent trait that is observed

from performance on particular cognitive tasks. Each ability is assessed by multiple

tests, which vary in how purely they tap the ability (the remainder of test variance is

made up specific test variance and cross-loadings on other factors that ideally are

small in magnitude). A disadvantage of these models is that they are dependent to a

certain extent on the properties of the tests in the battery, and on the testing sample

(Hunt, 2011). In addition, subjectivity remains in interpreting to what the factors

3

correspond at a more basic level, such as in a cognitive or biological sense (Hunt,

2010). An ultimate purpose of structural theories is to provide precise enough

delineations of factors that their biological bases can be discovered, although this

remains largely a future goal. Nonetheless, structural models have contributed

greatly to advances in intelligence research, and are an essential part of current

theories.

The three best-supported models in the intelligence literature are the extended

fluid-crystallized (Gf-Gc) model (Horn & Blankson, 2005), the Cattell-Horn-Carroll

model (CHC) model (McGrew, 2005, 2009), and the verbal-perceptual-image

rotation (VPR) model (Johnson & Bouchard, 2005b). The differences among these

models are covered in greater detail in chapter three, and are only summarized here.

The models diverge primarily at the second stratum of the intelligence hierarchy. 1

The CHC model contains the greatest number of second-order factors: ten that have

been firmly identified, and six more that have been characterized as “tentative”

(McGrew, 2009). The extended Gf-Gc model contains eight second-stratum factors,

which overlap strongly with those in the CHC model. This reflects the common

origin of the two models, which can be traced back to Cattell’s original fluid-

crystallized model (Cattell, 1963), and Thurstone’s primary mental abilities

(Thurstone, 1938). The VPR model, by comparison, is more parsimonious and

proposes only three second-stratum factors (the factors for which it was named).

The most notable difference between the second-stratum factors in the three

models is that the CHC and Gf-Gc models contain factors which are delineated by

how much they tap so-called fluid versus crystallized ability. Fluid intelligence

refers to the ability to learn new information and solve novel problems, without

regard to knowledge content or the content of material to which reasoning is to be

applied, whereas crystallized intelligence refers to knowledge acquired from

previous learning experiences (Cattell, 1963). These two factors are both present in

the second-stratum of the CHC and Gf-Gc models. Moreover, the other factors can

1 There has been some confusion in the literature surrounding the term stratum. Typically, this term

has meant a level of a hierarchical model containing one or more factors. However, Johnson &

Bouchard (2005b) characterized the VPR model as having four strata, counting the first level of

individual tests as a stratum (p. 397). In the traditional sense, the model only has three strata. Reeve

and Bonaccio (2011) also inaccurately presented the VPR model as having four strata.

4

be distinguished into those based more on “process” (Gf) compared to those based on

“content” (Gc; Carroll, 1993). The VPR model, in contrast, posits that the second-

stratum factors are distinguished only by their content. The factors in the VPR

model are thought to be formed because the tests differ in the extent they are verbal

(requiring the understanding of words and symbols), perceptual (requiring the

understanding of visual-spatial stimuli), or image rotational (requiring the mentally

rotation of visual-spatial stimuli (Johnson & Bouchard, 2005b)

Three factor-comparison studies have compared the three models presented,

and in each case the VPR model displayed the best statistical fit (Johnson &

Bouchard, 2005a, 2005b; Johnson, Te Nijenhuis, & Bouchard, 2007). However,

these comparison studies relied on previous versions of the CHC model (the three-

stratum model; Carroll, 1993) and the extended Gf-Gc model (the Gf-Gc model

presented by Horn, 1998). Thus, a new comparison study was needed to distinguish

among the three models. This is presented in the third chapter. In addition, the best-

supported model was to be used in further examining associations with personality

and interests.

1.1.2 Personality

The dominant model in personality psychology is the Five-Factor Model

(FFM), which was first developed in factor analyses of personality trait terms by

Tupes and Christal (1961; reprinted in 1992) and Norman (1963). However, the

“Big Five” model only gained prominence in the mid-1980s after several different

researchers found new empirical support for the model and argued for its theoretical

merit (Goldberg, 1990; McCrae & Costa, 1985). Numerous labels have been put

forward for each of the factors; however, the most commonly-used names were

proposed by Costa and McCrae (1992): Extraversion, Neuroticism, Openness to

Experience, Agreeableness and Conscientiousness.

The trait approach to personality has itself undergone many criticisms since

its inception (Deary, 2009). Some have argued that the FFM is simply an empirical

taxonomy, and thus that it lacks theoretical explanations for what the personality

traits are, and how they emerge developmentally (Cervone, 2005; Cramer et al.,

2012). Notwithstanding these more basic issues surrounding traits, the FFM has

5

received considerable support as a taxonomic framework. The Five-Factor structure

has been studied and partially replicated in over fifty cultures (McCrae &

Terracciano, 2005, but see De Raad & Peabody, 2005). In addition, it has been

found to capture the variance of personality factors on other major scales, such as the

Eysenck Personality Questionnaire (Costa & McCrae, 1995), and Cattell’s 16PF

(Conn & Rieke, 1994, cited in McCrae, 2009).

There is the possibility that additional factors should be added to the Big

Five. For example, Ashton and Lee (2005) proposed a sixth dimension termed

Honesty-Humility, and up to eight broad factors have been supported by lexical

studies (De Raad & Barelds, 2008). Nonetheless, these studies have also recovered

the Big Five, supporting the position that they are “more-or-less sufficient to account

for the co-variation of most personality traits” (McCrae, 2009, p. 148). Cramer et al.

(2012) criticized the FFM for not being able to account for the variance in trait

ratings without cross-loadings; however, they did not specify a hierarchical model (in

the sense outlined above) that included facets. FFM proponents acknowledge that

facet-level variance is a significant part of the FFM model. As Ashton and Lee

(2012) observed: “Researchers have also known that a few broad factors can account

for some large fraction of the covariation among personality variables, and not for all

that covariation” (p. 433). Further refinement of the FFM at the facet and item level

is still ongoing (McCrae, 2009).

The PT personality scales were not developed according to the FFM, but as

described further in chapter 2, one study found a moderate level of correspondence

between the PT scales and the Big Five (Reeve, Meyer & Bonaccio, 2006). The

research on personality here was done in reference to the FFM, because it has proven

a useful taxonomy for personality psychology. Moreover, the FFM has been used in

much of the research aimed at discovering associations of personality with other

individual difference variables (Ackerman & Heggestad, 1997; Barrick et al., 2003),

and in the context of occupational prediction (Judge, Higgins, Thoresen, & Barrick,

1999). Thus the use of the FFM was helpful in forming hypotheses and relating the

results back to the literature.

6

1.1.3 Occupational interests

Similar to the situation in personality psychology, one model is predominant

in the occupational interests field: the RIASEC model (Holland, 1959, 1997). The

model is composed of six broad interest factors: Realistic, Investigative, Artistic,

Social, Enterprising, Conventional. The interest types are conceived as both

manifestations of different work environments, and people’s preferences for these

environments (Holland, 1959, 1997). The six types are organized in a hexagon, in

which the relations are expected to be highest between adjacent types, followed by

alternative types (types separated by one in the hexagon), whereas opposite types are

expected to have zero or negative associations. Consistent with these proposed

associations, Prediger (1982) analyzed Holland interest scores for career groups and

individuals and found support for two dimensions, named Data/Ideas and

People/Things. These dimensions spanned opposite types: People/Things contrasted

Social with Realistic interests, while Data/Things contrasted Conventional and

Enterprising on one side with Investigative and Artistic on the other. Figure 1.1

Displays the Holland hexagon and Prediger’s two dimensions. Hogan (1983) found

two similar dimensions, which he called sociability and conformity, although the

axes of these dimensions were rotated 30 degrees clockwise from Prediger’s

dimensions (Armstrong et al., 2011).

Figure 1.1 Holland’s interests hexagon

7

In addition to these two dimensions that appear to underlie the RIASEC

hexagon, a third dimension has been found termed prestige (Einarsdóttir & Rounds,

2000; Tracey & Rounds, 1996). This dimension was found when additional

occupations were added to the scale underlying the RIASEC typology: the vocational

preference inventory (VPI; Holland, 1985). These results suggested that the VPI has

a restricted range of occupational prestige, an observation that was confirmed when a

broader range of U.S. occupations was examined (Deng, Armstrong, & Rounds,

2007). In that same study, it was found that the prestige dimension was in fact not

orthogonal to People/Things and Data/Ideas. The prestige dimension was associated

with the Ideas pole of the Data/Ideas dimension. In addition, gender differences in

occupational preferences were strongly associated with the People/Things dimension.

Men tended to prefer jobs towards the Things pole, while women tended to prefer

jobs towards the People pole, as had been observed in previous studies (Tracey &

Rounds, 1992). Thus, the dimensions that underlie the RIASEC hexagon are related

to prestige and gender differences, but the prestige dimension is only observed if a

wider range of occupations than on the VPI is used (Deng et al., 2007).

To summarize, it appears that the RIASEC typology with its two dimensions

is a reasonably adequate description of occupational interests for occupations, but

this description could and probably should be expanded to give greater emphasis to a

third dimension of prestige. The RIASEC model of interests is the primary one used

in integrative research (Ackerman & Heggestad, 1997; Armstrong et al., 2008); thus

it was an important point of reference for the research presented here.

1.3 Integrative theories

This section focuses on integrative theories in general, and does not cover all the

literature on the overlap between interests, personality and cognitive abilities. For a

review of cognitive ability and personality, see chapter 4. For a review of cognitive

ability and interests, see chapter 5. The overlap of personality and interests was not

reviewed because it was not addressed in the research here. There were two primary

reasons for this. First, I suspected that cognitive abilities are the primary drivers of

associations and so wanted to focus first on their associations. Second, it was

8

necessary to keep the number of analyses in the study manageable (see chapter 5 for

further discussion).

Psychologists have long hypothesized and observed that cognitive abilities,

personality and interests are not entirely independent, but related; studies were

conducted as early as Pearson in 1906 (see Ackerman & Heggestad, 1997, for a

historical review). There are a number of possible reasons why a theory might be

sought to explain these associations. Traditionally, however, the theories have most

often been formulated in the context of explaining intellectual development

(Ackerman, 1996). After Cattell first conceived of the concepts of fluid and

crystallized intelligence (Cattell, 1943), the question emerged of how basic raw

ability (Gf) developed into acquired knowledge (Gc). Cattell proposed the

Investment Theory, which specified that Gc was the result of time invested, and of

interest levels for specific areas of knowledge (Cattell, 1987). Vernon (1961) also

theorized that industriousness and general academic interest both contributed

positively to “educational ability”, but early studies were hindered by small sample

sizes and a lack of broad measures for personality and interests.

As models in the three domains improved over time and measures become

more standardized, it became increasingly possible to gather results from diverse

studies. The first true meta-analysis of intelligence-personality associations was

conducted by Ackerman and Heggestad (1997). This study also included a more

qualitative review of the literature on interest-intelligence and interest-personality

associations. These associations were the basis for a new theory of intellectual

development called PPIK theory (intelligence-as-process, personality, interests, and

intelligence-as-knowledge) by Ackerman (1996). The theory shared several

similarities with investment theory, including maintenance of the distinction between

raw or fluid ability (intelligence-as-process) and crystallized intelligence

(intelligence-as-knowledge). Crucially, however, the theory specified that the

overlaps among intelligence, personality and interests took the form of four “trait

complexes”, which were defined as being similar to Snow’s concept of an aptitude

complex in the learning domain (Snow, 1989). Snow proposed there were

combinations of level of traits, such as cognitive abilities, personality traits and

9

motivational traits that statistically interacted to produce better or worse outcomes in

learning situations. Ackerman and Heggestad (1997) extended this concept to

acquisition of academic knowledge more generally. Moreover, they claimed that this

type of specialized knowledge is important to future occupation, a point addressed

below.

A different approach to the integration of individual difference across the

three big domains is to use occupational interests as an underlying framework

(Anthoney & Armstrong, 2010; Armstrong et al., 2008; Armstrong et al., 2011).

Armstrong and colleagues have proposed that the RIASEC model should be used

because of its focus on work environments. They argued that educational and work

environments are crucial to understanding the links among interests, personality and

cognitive abilities, because these environments create demands for these traits, thus

providing key contexts for them to become related (Armstrong et al., 2008). From

the opposite perspective, it is thought that the demands of different occupational

environments “pull” individuals towards them who have traits that would allow them

to meet those demands. Thus, educational and work environments are thought to

both have mutually reinforcing relations with traits, because they both select for the

traits and potentially enhance them once individuals are in the environments. These

ideas are not unique to Armstrong and colleagues, but have been proposed by a

number of theorists on the development of occupational interests (Gottfredson, 2005;

Hogan & Roberts, 2000; Scarr, 1996). One difference, however, is that Armstrong

and colleagues made the specific claim that the RIASEC framework can be used to

understand these relations.

Using a multiple-regression technique called property vector fitting,

Armstrong and colleagues have attempted to fit personality traits and cognitive

abilities onto the Holland hexagon (Anthoney & Armstrong, 2010; Armstrong et al.,

2008). Armstrong et al. (2008) contained three studies; the first used data from

several large studies that related the RIASEC types to personality traits (including

the Big Five) and work styles from the Jackson Vocational Interest Survey (Jackson,

1977). A two dimensional RIASEC circumplex was specified, and property vector

fitting was used to regress the personality and work style scores onto the two

10

dimensions. Of the 51 personality traits and work styles, two-thirds (34) of them had

more than 50% of their variance explained by the dimensions, indicating a good level

of integration into the framework. The distribution of the traits provided support for

both Prediger’s (1982) and Hogan’s (1983) underlying dimensions. In the second

and third studies of Armstrong et al. (2008), cognitive ability was integrated into two

and three dimensions of the RIASEC circumplex, which was also successful for a

majority of the abilities. However, one limitation of these analyses as compared with

Ackerman and Heggestad (1997) was that cognitive ability requirements were rated

for different jobs, but were not derived from intelligence tests. Similarly, in

Anthoney and Armstrong’s (2010) study self-ratings of cognitive abilities were

employed. Thus, there is a need to examine how actual cognitive ability scores fit

into these models. Another limitation of Armstrong and colleagues’ two studies was

that the framework was dependent to a large degree on the RIASEC model, which

likely does not give enough weight to job prestige (Deng et al., 2007).

A third possible integrative approach is to view interests and cognitive

abilities as part of personality, broadly considered. DeYoung (2011) proposed that

intelligence could be located underneath Openness to Experience in the FFM. In a

previous study on the facets of Openness to Experience, DeYoung found that they

were split into two domains: one labelled Openness which consisted of “aesthetically

oriented traits”, and the other called Intellect, which was formed from facets for

intellectual engagement or self-perceived intelligence (DeYoung, Quilty, & Peterson,

2007). General cognitive ability was most strongly correlated with the Intellect

aspect of Openness to Experience, as represented by the Ideas facet on the NEO PI-R

(DeYoung, Peterson, & Higgins, 2005). However, this is a simplified picture

because g has shown many smaller relations with at least one facet for each of the

Big Five (DeYoung, 2011). In addition, narrower cognitive abilities beyond g are

likely to have differential relations with personality traits. For example, Ackerman

and Heggestad (1997) found that Conscientiousness is associated with Conventional

interests in the RIASEC, which are in turn related to Perceptual Speed; thus it would

be predicted that Consciousness is also associated with Perceptual Speed, although

this association has not yet been observed directly.

11

From DeYoung’s (2011) viewpoint, occupational interests fit within

personality at the level of characteristic adaptations. Characteristic adaptation is a

concept taken from the personality theory of McCrae and Costa (2012); it is defined

as an acquired attribute, such as skill or attitude, that arises from the transaction of

the person with the environment. Characteristic adaptations are contrasted with the

basic tendencies that underlie the Big Five, which are thought to be more

biologically-based and resistant to environmental influence. In apparent opposition

to this view, behaviour genetic research has found that vocational interests display

similar heritability coefficients to personality traits (Lykken, Bouchard, McGue, &

Tellegen, 1993). However, Lykken et al. (1993) suggested that much of the

heritability of interests could be explained as an indirect effect of genetic influence

on other attributes, such as physique, personality, and cognitive ability. The

heritability of interests could also be the result of gene-environment interaction and

correlation of more basic traits; for example, if personality affects the initial selection

of learning environments, and the success of individuals in those environments

(Lykken et al., 1993). This hypothesis is echoed in a number of investment theories

(Bouchard, 1997; Gottfredson, 2005; Hogan & Roberts, 2000; Scarr, 1996).

Nonetheless, a major disadvantage of theories explaining occupational interests from

this perspective is that they do not contain the detailed predictions of trait overlap

that are provided in Ackerman’s PPIK theory and Armstrong’s framework.

The three integrative theories of personality, interests and cognitive abilities

can potentially be distinguished by examining how well their models of the overlap

match empirical data. PPIK theory proposes that this overlap is characterized by

four trait complexes that involve groupings of high levels of particular personality

traits, cognitive abilities and interests. The framework of Armstrong and colleagues

instead suggests that personality and cognitive abilities should be mapped as

continuous variables onto the RIASEC model of interests (Armstrong et al., 2011).

The interests are the primary focus because they refer to preferences for education

and work environments, which are theorized to be the contexts in which cognitive

abilities and personality become related to each other and to interests. Finally,

DeYoung (2011) has theorized that cognitive abilities and interests can be integrated

into the FFM model of personality, where cognitive ability is found primarily under

12

Openness to Experience, and interests are characteristic adaptations resulting from

the transaction of personality traits and cognitive abilities with the environment.

The last study of this thesis was focused first on examining the content of

ability-interest trait complexes of PPIK theory. There were two main reasons for

selecting PPIK theory. First, the proposed trait complexes were more parsimonious

and specific than the many possible overlaps between cognitive abilities and interests

in Armstrong’s and DeYoung’s approaches. This made them easier to identify and

potentially falsify. Second, Ackerman and colleagues put forth the hypothesis that

the trait complexes would demonstrate better predictive validity for occupation than

individual scores for the three trait domains. For example, Ackerman and Beier

asked: “is there a synergy among elements within the trait complexes, so that

concentrating on trait complexes is more informative in the career choice context

than individual trait measures?” (2003a, p. 209). This question provided another

prediction of PPIK theory to test.

1.3 Prediction of occupational type

The three integrative approaches presented thus far have been assessed on

their merits as theoretical frameworks, but a key issue is how they could potentially

improve our ability to understand and predict occupational attainment. While the

other theories have not involved as strong a claim for predictive power as PPIK

theory, predicting occupation is a stated goal for most research in this area

(Armstrong et al., 2008). As cognitive ability and personality are both related to and

predict occupation (De Fruyt & Mervielde, 1999; Schmidt & Hunter, 2004), it is

logical to hypothesize that integrative theories could provide superior prediction to

considering each of the domains separately.

PPIK theory has a notable advantage over the frameworks of Armstrong et al.

(2008) and DeYoung (2011), in that it has existed for a longer time, and thus more

research has been done to link the theory to real-world outcomes. Ackerman and

colleagues have found that their trait complexes relate to academic knowledge

(Ackerman & Rolfus, 1999), university course selection (Ackerman, 2000) and

university course performance (Kanfer, Wolf, Kantrowitz, & Ackerman, 2010). The

results were taken as support for PPIK theory because knowledge is hypothesized to

13

act a mediator between trait complexes and occupational attainment (Ackerman,

1996). Nevertheless, these studies only provide indirect evidence for theory because

the predictive validities of either trait complexes or knowledge have not been

examined for attained occupation.

In contrast to the indirect evidence for PPIK theory, there is not yet any

evidence that the approach of Armstrong and colleagues could improve the

prediction of occupation. This would require research relating the framework to

occupational outcomes, possibly comparing its predictive validity to other theories.

Previous research has demonstrated that personality and cognitive abilities can be

mostly effectively integrated into two or three RIASEC dimensions (Anthoney &

Armstrong, 2010; Armstrong et al., 2008). As the RIASEC dimensions are linked

closely with preferences for different educational and occupational environments

(Holland, 1997), the model with these dimensions could be useful in predicting

future occupation, but this remains hypothetical.

DeYoung (2011) has provided a theoretical argument for how cognitive

abilities and interests can be fit into the FFM. However, this account remains very

general and does not specify, for example, which personality traits are involved in

the formation of which occupational interests, or how narrow cognitive abilities fit

into the FFM. Without these details it is not yet possible to use this theory to predict

occupation.

Of the three integrative theories in the literature, PPIK is the most developed.

It has made the most specific predictions for the overlap between cognitive abilities,

personality and interests, and some indirect evidence has been found that trait

complexes relate to occupation. For these reasons, I chose to test this theory in

Project TALENT.

14

Chapter 2: Project TALENT’s design and measures

Project TALENT (PT) was a study approved by the U.S. Department of

Education in 1959 (Flanagan, et al., 1962). It was first conceived by John C.

Flanagan, a professor of psychology at the University of Pittsburgh, who became the

principal investigator. It was designed to be a longitudinal and nationally-

representative study of the human talent of high school students, examining how this

talent could be better identified and promoted. For example, the U.S. Commissioner

of Education stated that the project was “an attempt to determine why so much of the

nation’s human potential is lost and what schools, counselors and parents can do to

reduce the loss” (p. 1, Flanagan, 1962). To this end, a large amount of information

was to be collected about the students (e.g. their aptitudes, interests and social

backgrounds), as well as schools (e.g. their resources and teaching methods). The

following description of the study relies heavily on the first PT report (Flanagan, et

al., 1962). Details of the testing materials are also provided in the Project TALENT

handbook (Wise, McLaughlin, & Steel, 1979). The computerized PT data was

compiled by the American Institutes for Research, a nonprofit social science research

institute founded by Dr. Flanagan. The data is available through the National

Archive of Computerized Data on Aging (NACDA), from which they were obtained

for the current research.2

The advisory panel of Project TALENT and its staff designed the study and

its measures in 1958 and 1959. The schools were selected using a stratified random

sample of public and private high schools across the United States. In all, 1353

schools were eventually sampled (93% of those asked), and approximately 440,000

students, who represented approximately 5% of the total American high school

population. To enable the administration of the tests in each area, 90 regional

coordinators were employed. The regional coordinators were primarily

psychologists who were asked to work with local school administrations and

teachers. Teachers and guidance counselors were trained to administer the Project

TALENT tests, which they gave over two days. The initial testing occurred in

2 The website of the NACDA is http://www.icpsr.umich.edu/icpsrweb/NACDA

15

March of 1960. Follow-up mail surveys were conducted after the students completed

high school, after the intervals of 1, 5 and 11 years. The follow-up surveys asked

about the participants’ personal, educational and career experiences. Most relevant

to the study presented in chapter 5, participants were asked about their current

occupations at those times. In this chapter the data used in the thesis are first

described: the measures of intelligence, personality, occupation interests, and follow-

up occupational status.

2.1 Intelligence tests

The PT aptitude and achievement tests were newly-designed for the study.

Their stated purpose was to “survey a variety of human aptitudes and to obtain scores

which might predict an individual’s ability to develop those aptitudes for vocational

and educational success” (p. 57, Flanagan et al., 1962). One of the main reasons that

new tests were created is that it was felt that pre-existing intelligence tests did not

survey a wide enough variety of aptitudes, partly because the individual subtests

were too long, and the ones in PT should be shorter. In addition, this would make it

certain that none of the students had been previously exposed to the new tests.

A first experimental battery of all the tests was given to a sample of

approximately 6000 high school students, in schools in the Northeast, South and

Midwestern U.S. (Flanagan, et al., 1962, p. 60). Item-level analysis was used to

exclude items that were unreliable, too hard, or too easy. Following this process, the

final 60-test version was developed. The battery was composed of two main

sections: the information tests, and the specific aptitude and achievement tests. For

detailed test descriptions see chapter 3; here their general purpose and design is

outlined.

The information tests were multiple-choice knowledge questions on a very

broad range of topics, including both general knowledge and academic subjects.

There were 36 subtests that ranged from 2 to 24 items in length. There were several

purposes to the information tests. Firstly, it was held that the breadth of a person’s

knowledge was a measure of general intelligence; similar information tests were used

in this way in the Army Alpha and Otis Mental Ability batteries. Second, the more

specific tests had the potential to capture achievement in particular areas, as well as

16

interest and motivation towards those topics, such as physical science, fine art or

sports. Third, there was a vocabulary scale, which was regarded as a measure of

verbal intelligence. In practice, the usefulness of many of the smaller information

subscales was limited because of their narrow topics and poor reliability (Cureton,

1968; Flanagan et al., 1964). Cureton (1968) recommended that tests with less than

nine items be excluded for intelligence research, which eliminated 15 tests. In

addition to this, there were a number of tests that were highly likely to be sex-biased.

For example, tests of Sports and Farming information favoured boys, whereas the

Home Economics tests required knowledge to which girls were more likely to be

exposed. Avoiding unreliability and sex-bias meant that only a maximum of 16 out

of 36 tests (44%) were used in the studies of this thesis.

There were 24 aptitude and achievement tests in the PT battery. The types

and number of aptitude tests were as follows: verbal (3), spatial visualization (2),

reasoning (3), memory (2) and processing speed (4). The achievement tests

included five English tests and three Math tests. The English tests assessed basic

writing and reading skills learned in school, and the Math tests assessed arithmetic,

introductory Math (studied in 9th

grade) and advanced Math (studied in grade 10 or

later). The advanced Math test was left out of all studies here because it was deemed

to be unfair for younger students. Generally, all of the aptitude and achievements

tests were used in assessing cognitive ability in the present research, because most

did not require prior knowledge, and even those that did (e.g. the English tests), only

demanded basic knowledge to which all students should have been exposed.

2.2 Personality tests

There were 150 personality items in PT, which were in a section entitled

“Student Activities Inventory”. Students were asked to respond to statements about

behaviors or characteristics in terms of how well they described “the things I do and

the way I do them”. Reponses were on a five-point Likert scale. The scores on the

items were summed to form 13 scales; however, three of the scales were

experimental and were not electronically recorded. Thus, there were ten personality

scales made up of 108 items, with 7 to 24 items per scale. Item-level data were not

available from the PT dataset, only scale scores.

17

The scales assessed general personality, but were aimed at personality traits

that were important in educational and occupational contexts. For example,

Flanagan et al. (1962) stated that “the TALENT [personality] battery was based on

the hope that it would eventually add to our knowledge of how personality

differences help to account for the differences in accomplishments of equally

talented normal people” (p. 130). Thus, the scales have strong representation of

traits that would be classified under Conscientiousness in the Big Five. However,

Reeve, Meyer and Bonaccio (2006) re-administered the PT items to 219 university

students, and found that the scales spanned all of the Big Five (as assessed by the

NEO-PI-R). In a joint factor-analysis with the NEO, each of the Big Five received at

least one substantial loading (mean r = .70, range = .51 to .81) from the PT scales.

Another difference of the PT personality scales from conventional personality

assessment is that the students were aware that the purpose of the study was to

examine talent, and it was conducted in a school context. Thus, although the

instruction to students was to reflect on their general behavior, the context may have

influenced them to respond in a manner more consistent with how they perceived

themselves within school, or how they wished to portray themselves in a school

setting. This possible confounding factor is explored in greater detail in chapter 4.

2.3 Occupational interest tests

The Interest inventory of PT was composed of 205 items, of which 122 were

occupation titles and 83 were occupation-related activities. Students were asked to

indicate how much they would like to do the occupation or activity. The PT study

designers evaluated pre-existing interest scales, such as Strong’s Vocational Interest

Blank and Kuder’s Preference Record, but decided to construct new scales.

The interest items were compiled into seventeen scales by PT investigators,

based on a priori classification of different occupational areas. Using fifteen

independent raters, Reeve and Hakel (2000) found that all of the PT interest scales

except one (Labour) could be assigned to the RIASEC categories with acceptable

accuracy (inter-rater agreement of 66% or higher). Unlike the personality scales,

however, item-level data were available for the interests. As the original PT interest

18

scales were not created on an empirical basis, in chapter 5 new scales were derived

by factor analysis.

2.4 Occupation at follow-up

The PT follow-up data were collected primarily by the use of mail

questionnaires. The questionnaires were designed to give a broad overview of the

participants’ lives after high school, thus in addition to questions on educational and

occupational experience, they were asked about their marital status, quality of life,

health, and other social variables (Wise et al., 1979).

Of the greatest relevance to the current research were the 11-year follow-ups,

which occurred in 1971 to 1974, when the participants were approximately 28 years

of age. Current occupation was asked in a written response, which was originally

transformed into over 1000 occupation codes. These specific codes were later

reduced to 254 job codes representing specific jobs or job areas such as Airplane

Navigator, Veterinarian or Metal Trades (Wise et al., 1979). The job titles were also

organized into twelve categories according to broad occupational themes. Greater

detail on the occupation categories and the frequencies of participants in each are

provided in chapter 5.

Although efforts were made to contact all PT participants, the participation

rates for each subsequent follow-up decreased. Much of this attrition was due to lack

of the most recent addresses for participants; addresses were lost for approximately

5% of participants for each year, in each grade, compared to baseline. Response

rates also decreased with the time between the baseline testing and follow-ups, which

were longer for the participants who were in lower grades (younger) at baseline. For

the 11-year follow-up, 28.8% of the grade-12 participants returned the

questionnaires, but only 19.6% of the grade-9 sample. To deal with attrition and the

lack of representativeness of the follow-up samples, PT investigators conducted

special interviews with approximately 2500 non-respondents to the mail

questionnaires. The missing participants were found by a variety of methods, such as

searching telephone directories, asking the Department of Motor Vehicles, and

contacting the high school for new addresses. Once participants were located they

were given telephone or in-person interviews (Wise et al., 1979). Sample weights

19

were created in accordance with the sampling ratio of the special sample to original

the 1960 sample (Wise et al., 1979). These sampling weights could then be used to

adjust the follow-up sample to be representative of the baseline sample. The

sampling weights were used in chapter 5 when follow-up occupation was being

investigated.

20

Chapter 3: Comparing the VPR, CHC and Extended Gf-Gc models

3.1 Introduction

Disagreement about the structure of intelligence has a long history in psychology.

Recently, however, some researchers have proposed that a consensus theory has

emerged in the form of the Cattell-Horn-Carroll (CHC) theory of cognitive ability

(Benson, Hulac, & Kranzler, 2010; Flanagan, Ortiz, & Alfonso, 2007; McGrew,

2005, 2009). McGrew (2005), for example, asserted that: “[Carroll’s synthesis] has

finally provided both intelligence scholars and practitioners with the first

empirically-based consensus Rosetta stone from which to organize research and

practice” (p. 171). This view was contradicted, however, by three recent studies in

which an updated version of Vernon’s verbal-perceptual model (1961, 1965) was

found to provide better fit to large intelligence test batteries than the two precursors

of the CHC model: Horn and Cattell’s fluid-crystallized (Gf-Gc) model (Cattell,

1963; Horn & Noll, 1997) and Carroll’s (1993) three-stratum model (Johnson &

Bouchard, 2005a, 2005b; Johnson, Te Nijenhuis, et al., 2007). Nevertheless, these

previous comparison studies relied on an interpretation of Gf-Gc theory which only

included fluid and crystallized ability as second-order factors, whereas the Extended

Gf-Gc theory contains six more such factors (Horn & Blankson, 2005). In addition,

CHC theory has largely supplanted the three-stratum theory, and contains a number

of differences from it (see below, and McGrew, 2009, for details). The current study

was thus aimed at providing an updated test of whether the verbal-perceptual-image

rotation (VPR) model (Johnson and Bouchard, 2005a), the CHC model, or the

Extended Gf-Gc model provides a better description of the structure of intelligence.

Deciding among these models is an important issue for intelligence researchers

because each implies a different underlying theory about the nature of intelligence

and its manifestation. The three models have also each received substantial

empirical support (Carroll, 1993; Horn & Noll, 1997; Hunt, 2011; Johnson &

Bouchard, 2005b). The fluid-crystallized model has arguably been the most

influential theory of intelligence to date in terms of the frequency of its application in

21

research and test development (Carroll, 1993; Horn, 1998; Mackintosh, 2012). As

Kaufman (2012) recently observed “the core concepts of Gc and Gf are still universal

to nearly all IQ tests” (p. 119). It has also been claimed that the CHC model has the

most cumulative factor-analytic evidence supporting it, thanks in large part to

Carroll’s (1993) major synthesis (McGrew, 2009). Although the VPR model has not

been as prominent in the literature as the other two, we argue below that it has a

number of advantages over the Gf-Gc and CHC models.

The main features of the three models are outlined in Table 3.1. These features

are highlighted because they best reflect the theoretical differences among the

models. Although these differences are based upon the most recent versions of the

models, they have their roots in longstanding disagreements about the structure of

intelligence. The CHC and Gf-Gc models are products of the American school of

intelligence research, while the VPR model has its origins in the British school

(Vernon, 1961, Carroll, 1993). In the early twentieth century, the divergent views of

these two schools on the structure of ability were represented by Spearman and

Thurstone. Spearman and his fellow British psychologists such as Burt (but not

Thomson) emphasized the importance of the general factor of intelligence (g) over

group factors in the structure of cognitive ability, whereas American psychologists,

led by Thurstone, supported a model of orthogonal group factors, named primary

mental abilities, with no general factor (Thurstone, 1938). Spearman and his

colleagues argued that Thurstone’s seven to nine primary factors were correlated and

thus could also yield a model with a general factor and smaller group factors

(Eysenck, 1939; Speaman, 1939). Whereas Thurstone rather quickly acknowledged

the presence of higher-order factors in his datasets (Thurstone, 1947, cited in Carroll,

1993), and helped to develop the techniques for higher-order factor analysis, his

reluctance to accept Spearman’s g, and his conception of independent primary

mental abilities had a lasting influence upon American intelligence researchers

(Carroll, 1993). Notably, Cattell and Horn followed Thurstone in not accepting a g

factor in their Gf-Gc model (Horn & Noll, 1997). In contrast, the g factor was

prominent in Vernon’s verbal-perceptual model (1961), and remains so in the VPR

model. Nevertheless, it should be noted that VPR theory is agnostic about whether g

represents a reflective or formative variable. We took the latent factor model

22

approach to the VPR model in the current study, yet the model could be reformulated

to conform to other approaches to the positive manifold (D. J. Bartholomew, Deary,

& Lawn, 2009; Van Der Maas et al., 2006).

Table 3.1

Primary features of the CHC, Extended Gf-Gc and VPR models.

Feature CHC model Extended Gf-

Gc model

VPR model

g factor postulated? yes no yes

Number of second-order

factors

10 (plus 6 more

“tentatively

identified”)

8 3

Second-order factors are

distinguished as content

factors versus raw ability

factors, or by content only.

Content (Gc, Gq,

etc.) versus raw

abilities (Gf, Gsm,

etc.) a

Content (Gc)

versus raw

abilities (Gf,

Gs, etc.) b

Content only

Number and nature of first-

order factors

Pre-specified Pre-specified Left to battery

content

a Gq is quantitative knowledge, Gsm is short-term memory (see McGrew, 2009).

b Gs processing speed (McGrew, 2009).

Although the CHC model does contain a g factor, its second-order factors are

highly similar to those in the Gf-Gc model. This is because the CHC model was

formed by merging Carroll’s (1993) three-stratum model with the Gf-Gc model

(McGrew, 1997, 2005). Carroll himself was also strongly influenced by Gf-Gc

theory, writing that prior to his theory it was “the most well-founded and reasonable

approach to an acceptable theory of the structure of cognitive abilities” (p. 62, 1993).

The original Gf-Gc model had only two second-order factors of fluid and crystallized

ability; however, Cattell and his student Horn eventually added six other second-

order factors. These latter factors resemble Thurstone’s primary abilities: for

instance, quantitative knowledge (Gq), which is similar to the primary ability

numerical facility, visual processing (Gv), which is similar to the primary ability

spatial relations, and processing speed (Gs), which is similar to the primary ability

perceptual speed (Horn & Blankson, 2005; Mackintosh, 2012; McGrew, 2009). Due

to the interdependence of the Gf-Gc and CHC models, analogous factors are present

23

in the CHC model. Thus, the number and nature of the second-order factors in both

models can still be traced back to Thurstone’s primary abilities. The CHC and Gf-

Gc models are distinguished at the second-order level chiefly because the CHC

model has several additional factors, most notably, reading and writing ability (Grw)

and domain-specific knowledge (Gkn); these two factors are instead subsumed by Gc

in the Gf-Gc model (Horn & Blankson, 2005; McGrew, 2009).

P. E. Vernon, who was a contemporary of both Thurstone and Cattell, proposed

the first hierarchical model of intelligence in 1950 (Vernon, 1961). In contrast with

Thurstone’s primary factor model, Vernon’s verbal-perceptual model contained a g

factor and only two broad second-order group factors: the v:ed factor subsumed first-

order factors for verbal, scholastic and numerical ability, and k:m was formed by

loadings from first-order factors of mechanical information, spatial ability, and

perceptual and psychomotor abilities (Vernon, 1961, 1965). Johnson and Bouchard

(2005a) found that the addition of a second-stratum Image Rotation factor

significantly improved the fit of the verbal-perceptual model, and thus they proposed

the Verbal-Perceptual-Image Rotation (VPR) model as an extension of Vernon’s

model. This return towards a more parsimonious model similar to Vernon’s was also

anticipated by researchers such as Undheim (1981) and Gustafsson (1984).

The third feature in Table 3.1 indicates that the broad group factors in the VPR

model are characterized by the subject-matter content of the tests (Johnson &

Bouchard, 2005b). In the CHC and Gf-Gc models there is instead a contrast between

factors which are theorized to involve more basic process abilities (e.g. Gf, Gsm),

and those which are thought to be measures of acquired knowledge (e.g. Gc, Gq)

(McGrew, 2009; Horn & Blankson, 2005). For example, Carroll (1993) stated that:

“the [second-order] domains appear to differ in the relative emphasis they give to

process, content and manner of response” (p. 634). However, as mentioned above,

this distinction between fluid and crystallized factors was not supported in previous

model-comparison studies where the VPR model outperformed Gf-Gc and three-

stratum models (Johnson & Bouchard, 2005a, 2005b; Johnson, Te Nijenhuis, et al.,

2007). In fact, Johnson and Bouchard (2005a) found that even in Cattell’s test

battery designed according to Gf-Gc theory (the Comprehensive Ability Battery;

24

Hakistian & Catell, 1975), the verbal-perceptual distinction was better supported

than the fluid-crystallized one (as assessed by model fit).

The fluid-crystallized division also leads to theoretical problems for the Gf-Gc

and CHC models. In the Gf-Gc model the contrast between ability and knowledge

domains is emphasized to the exclusion of a g factor (Horn & Blankson, 2005);

however, the g factor has been supported in almost all factor-analytic studies where it

was possible to find one (Carroll, 1993; Jensen, 1998) and the Gf and Gc factors

have a correlation as high as .85, supporting an underlying g factor (Johnson &

Bouchard, 2005b). In the CHC model, the presence of both a g factor and a Gf factor

is problematic because of their theoretical similarity: both factors have been

described as involving the ability to reason and profit from experience across many

cognitive domains (Carroll, 1993; Cattell, 1987). For example, Carroll (1993) stated

that: “in the main, I accept Spearman’s concept of g, at least to the extent of

accepting for serious consideration his notions about the basic process measured by

g—the apprehension of experience… and the eduction of relations and correlates” (p.

637), while Cattell wrote of the Gf factor that it was “a single relation perceiving

capacity” that could be invested in any cognitive domain (1987, p. 138, cited in Kan,

Kievet, Dolan, & van der Maas, 2011). This theoretical redundancy has been pointed

out by several authors, and a number of studies have found that g and Gf factors are

statistically indistinguishable (Gustafsson, 1984, 1988, 2002; Kan et al., 2011; Keith,

Fine, Taub, Reynolds, & Kranzler, 2006; Kvist & Gustafsson, 2008; Undheim &

Gustafsson, 1987); however, these models also tend to generate often

unacknowledged out-of-range parameter estimates, such as negative residual

variances. Recently, Kan et al. (2011) also found that, in participants with equal

educational backgrounds, the Gc factor was identical with verbal comprehension,

which is in conflict with the theoretical interpretation of Gc in Cattell’s investment

theory, but consistent with the role of the verbal factor in the VPR model (Johnson &

Bouchard, 2005b).

The last feature which distinguishes the VPR model from the Gf-Gc and CHC

models is the number of and nature of the first-order factors. Along with Spearman

and other early British intelligence researchers, Vernon (1961) criticized American

25

investigators for accepting too many group factors in the lower orders of the

intelligence hierarchy; he argued that this was due to overly lax selection criteria and

because their factor-analytic methods assigned some of the g variance to group

factors. In favor of his more limited set of broad group factors, Vernon noted that

v:ed and k:m emerged in any representative battery of tests, whereas the narrower

(generally, first-order) factors proposed by American psychologists were very

dependent on the particular tests administered and the selectiveness of the sample

(see Appendix in Vernon, 1961). This lack of certainty about narrow factors is

maintained in the VPR model, in that it does not make specific predictions about

which first-order factors should emerge in a given test battery, instead leaving the

characters of the factors to vary according to the specific tests in the battery (Table

3.1). Vernon (1961) also offered a pragmatic argument against naming and

including narrow ability factors in the structure of intelligence; he observed that

often the narrow factors in intelligence test batteries did not add substantial

incremental variance to the prediction of educational or occupational performance,

over and above g and the broad group factors. This objection is not taken into

account by CHC investigators, who aim to include every factor identified in

intelligence research in the CHC theory/model (McGrew, 2009), regardless of

whether they add significant incremental validity over higher-order factors towards

predicting outcomes of interest. Proponents of the Gf-Gc model also maintain that

first-orders factors should be named, and that the factors which should emerge for a

given battery can be pre-specified (Horn & Blankson, 2005).

In spite of claims for its status as the leading intelligence theory (McGrew, 2009),

there are still empirical reasons to doubt whether the CHC model provides an

accurate picture of the overall structure of intelligence. Carroll’s (1993) three-stratum

theory was based on his interpretation of numerous exploratory factor analyses, not

on confirmatory factor analysis, which allows the researcher to investigate and

control many more aspects of the measurement model, and, especially, to pit

competing models against each other empirically. Second, the vast majority of the

datasets re-analyzed by Carroll were not suited to determining the broad higher-order

structure of ability: the CHC model contains at least ten second-order factors, but all

except two of Carroll’s 461 datasets contained three or fewer second-order factors.

26

Although the broad CHC factors have been supported in a number of more recent

studies (see McGrew, 2005, for summary), much of this research has been performed

on test batteries designed within the CHC framework or its precursors, and

competing models have not been compared with it. These criticisms also apply to

the factor-analytic evidence supporting the Extended Gf-Gc model (Horn &

Blankson, 2005).

In order to establish whether the CHC, Extended Gf-Gc, or VPR model is the

best-supported, further confirmatory studies are needed which compare their

predictions in test batteries that were not constructed according to any particular

theory of intelligence. As mentioned above, previous studies have not examined the

most recent versions of these models, thus this was the main purpose for the current

study.

3.1.1 Previous factor-analytic research on Project TALENT

The current study was undertaken with data from Project TALENT, which was

a longitudinal study on American high school students that was designed to

investigate their aptitudes, interests, and backgrounds, and the influences of these

variables on educational and occupational outcomes (PT; Flanagan et al., 1962).

During Project TALENT, 60 aptitude and achievement tests were given to a very

large and nationally-representative sample of the U.S. student population (see

Methods below for more details). As detailed below, three PT datasets were also

analyzed by Carroll (1993), which provided a basis for the development of the CHC

model in our study. The data are thus of particular relevance to the question of

which of the three models provides the best fit. In order to provide context for the

factor analysis of this test battery in this study we first review notable previous

analyses of these data.

The first report which contained an analysis of the aptitude and achievement

tests in PT was written by the research group who designed the study (Flanagan et

al., 1964). Instead of performing factor analysis, however, Flanagan et al. (1964)

examined correlation matrices and uniqueness coefficients of the tests. Using this

method they tentatively identified and labeled seven common factors: general verbal

27

ability, reasoning, rote memorization, spatial visualization, visual perception, speed

of response, and information in the mechanical-electric-electronic domain.

Lohnes (1966) ran principal components analysis on a combined sample of

grades 9 and 12 participants from Project TALENT. He used all 60 test scores in the

battery and extracted eleven factors, excluding factors for grade and sex. However,

four of these factors were highly specific (such as information on etiquette, or

hunting and fishing); subsequent investigators have typically excluded a number of

these information tests because of their highly specific nature, but also because they

contained a small number of items and many had low reliability coefficients (see

Flanagan et al., 1964).

Shaycroft (1967) examined the changes in 47 PT test scores from grade 9 to

grade 12, and performed principal-axes factor analysis on the tests. She retained

seven broad factors, as Lohnes (1966) did. However, the most extensive factor

analysis of PT tests in this period was performed by Cureton (1968), who provided

detailed comparisons of his factors to those in Lohnes (1966) and Shaycroft (1967).

Cureton’s (1968) sample consisted of 543 students from Project TALENT who

also completed three other intelligence test batteries. He performed three different

factor analyses: on all the tests combined, the non-Talent tests only, and the PT tests

only. For the PT test analysis Cureton (1968) excluded all the information tests with

less than nine items, and ran principal-axes factor analysis with oblique rotation. He

accepted seven factors, and although these differed slightly by sex, each model

included a factor which combined the English and Math tests, a verbal-information

factor, a clerical-perceptual factor, as well as factors for spatial reasoning,

mechanical/outdoor knowledge, math and memory. These factors were generally

consistent with those in Lohnes (1966) and Shaycroft (1967), despite different

factoring methods and selection of tests in the three studies. Importantly, Cureton

(1968) also observed that the mechanical factor had a tendency to combine with the

spatial factor, and that the verbal-information factor was closely related to the

English and Math factor; thus Cureton (1968) observed that “though second-order

and hierarchical analysis was not used, the results are in striking accord with the

theory of cognitive abilities outlined by Vernon” (p. 71).

28

When Carroll (1993) re-analyzed the data from Project Talent, he revisited the

analyses of Flanagan et al. (1964), Shaycroft (1967) and Cureton (1968), which is a

reflection of the lack of intervening factorial research after these seminal studies.3

Carroll (1993) accepted seven first-order factors in his re-analysis of the grade 9 data

from Shaycroft (1967), and his factors were very similar to those found by Shaycroft

and Cureton, except that he did not find the Math and English tests to combine to

form a factor. Despite the fact that these seven first-order factors were underneath

four different second-order broad factors according to the three-stratum model,

Carroll (1993) obtained only one second-order factor for the male data, which he

classified as Gc (see dataset codename SHAY01; Carroll, 1993). In the female data

(SHAY02), Carroll extracted three second-order factors: 2H, 2V (broad

visualization) and a technical knowledge factor; he also found a third-order g factor.

Carroll’s (1993) analysis of Flanagan et al. (1964) was based on a correlation matrix

which did not include the information tests (FLAN01). He extracted five first-order

factors from these tests: verbal ability (V), math knowledge (KM), English language

usage (EU), visualization (VZ) and perceptual speed (P). According to the three-

stratum model, these factors should have loaded onto three separate higher-order

factors, but Carroll (1993) only obtained one factor, which he characterized as 2H (a

combination of fluid and crystallized intelligence). Together, the re-analyses by

Carroll suggest that the higher-order structure of the PT tests is more parsimonious

than implied by three-stratum theory, and thus potentially Gf-Gc and CHC theory as

well.

Three more recent studies using PT data took Carroll’s (1993) factor solutions

as a starting point (Reeve, 2004; Reeve & Heggestad, 2004; Reeve et al., 2006).

Although Reeve and colleagues found acceptable fit for their confirmatory factor

analysis (CFA) measurement models, these studies were not primarily aimed at

investigating the structure of the PT test battery, and contained no exploratory factor

analysis (EFA) to determine the number of first-order factors for the selected tests,

nor higher-order factor analysis.

3 Caroll’s (1993) re-analysis of Cureton (1968) was based on the dataset with the PT tests combined

with three additional test batteries, and hence is not as relevant to the current review as Carroll’s two

other re-analyses.

29

In the present study we first performed EFA in order to establish the first-order

structure of the PT tests. This provided an objective basis upon which to perform

higher-order CFA to test the relative fits of the CHC, Extended Gf-Gc and VPR

models.

3.2 Methods

3.2.1 Sample

The participants in Project TALENT (PT) were drawn from a stratified

random sample of all public and private high schools in the United States in 1960

(Flanagan et al., 1962). The full obtained sample consisted of 376,213 students, with

approximately 100, 000 students in each grade from 9 through 12. Of the full

sample, 50.13% was female. The age range was from a mean of 14.4 in grade 9 (SD

= .78) to 17.3 in grade 12 (SD = .67). The full individual age range was from 8 to

21.

3.2.2 Measures

Short descriptions and reliabilities of each cognitive ability test used in the

current study are presented in Table 3.2. In addition to aptitude tests, the designers

of PT included a large number of multiple-choice information tests because they

sought to use these to predict future educational and vocational success in a wide

variety of areas (Flanagan et al., 1962). The information tests were based partly on

knowledge acquired from formal education, but were also designed to assess self-

motivated learning outside the classroom (Flanagan et al., 1962). The information

tests were also designed to be non-redundant with the achievement tests; thus the

math information test contained factual items on mathematical concepts, but did not

require problem solving as did the arithmetic and math achievement tests (Flanagan

et al., 1962).

Following previous analysts such as Cureton (1968), we excluded tests with

less than eight items because of their low reliabilities and tendency to form highly

specific factors. An effort was also made to exclude information tests that were

likely to be sex-biased due to unequal learning opportunities for boys and girls, such

as the Sports, Farming and Home Economics tests. Nonetheless, the Aeronautics and

30

Space, Electricity and Electronics and Mechanics tests were retained because of their

importance in distinguishing the VPR from the CHC and Gf-Gc models; the

perceptual factor in the VPR model is formed from a combination of spatial and

mechanical-knowledge factors, whereas these factors load onto separate second-

stratum factors in the CHC and Gf-Gc models (Johnson & Bouchard, 2005b; Horn &

Blankson, 2005; McGrew, 2009). The advanced mathematics test (R333) 4

was

excluded because it included material that was not taught until grades 10 through 12

(Wise et al., 1979); thus it was deemed to be an unfair test for grade 9 students.

The remaining PT battery still contained 16 information tests that we

considered were possibly less relevant to cognitive ability than the aptitude tests.

Flanagan et al. (1962) defended the information tests as indicators of general

intelligence based on their inclusion in classic intelligence test batteries such as the

Army Alpha test and the Otis Mental Ability Tests, but they also noted that

information tests are measures of interest and past achievement in specific areas

(such as Biology, Physics, Literature, etc.). To defuse this question about the

appropriateness of including the information tests, we fit our models to two

selections of tests (hereafter termed the broad and narrow selections). The broad

selection included the information tests, and consisted of 37 tests in total. The

narrow selection excluded the information tests except for Vocabulary, and consisted

of 22 tests.

4 Variable ID numbers that were assigned by Project TALENT are occasionally referenced, in order to

clarify which tests are being discussed.

31

Table 3.2

Project Talent test names, short descriptions, and reliabilities for males/females.

Test Name Description Items Reliability

Vocabulary General vocabulary questions. 21 .71/.71

Literature Items on a broad selection of literary works. 24 .72/.70

Music Musical information (not requiring formal training in music). 13 .67/.67

Social Studies Items on facts and concepts from history, economics, civics,

geography and current affairs.

24 .83/.79

Mathematics Items on mathematical information and concepts. 23 .81/.78

Physical Science Items about chemistry, physics, astronomy, and other

physical sciences, not necessarily acquired through formal

education.

18 .77/.72

Biological Science Questions about botany, zoology and microbiology. 11 .57/.51

Aeronautics and Space Items on flying technique, navigation, jet planes, and space

exploration

10 .63/.34

Electricity and

Electronics

Items on the construction and maintenance of electrical or

electronic equipment.

20 .76/.43

Mechanics Information on automobiles, common machines, etc. 19 .66/.48

Art General knowledge about art, artists and art works. 12 ..64/.65 a

Law General knowledge items that could be acquired through

books or news reports on legal affairs.

9 .51/.43 a

Health Items on practical health maintenance, nutrition and common

health care techniques.

9 .58/.55 a

Bible General knowledge about the characters and teachings of the

Bible.

15 .74/.73 a

Theatre and Ballet General terms from theatre and ballet. 8 .55/.59 a

Miscellaneous Miscellaneous knowledge questions. 10 .48/.42a

Memory for sentences Recalling a missing word from a memorized sentence. 16 .62/.63b

Memory for words Recalling an English word that corresponds to a word in a

(fictional) foreign language.

24 .80/.83b

Disguised words The ability to use phonetic sound to puzzle out which familiar

English word a nonsense word corresponds to.

30 .86/.87c

Spelling Items testing the ability to spell, and not the size of

vocabulary.

16 .60/.56

Capitalization Items requiring the correct capitalization of words in a

sentence.

33 .85/.83

Punctuation Items on the appropriate use of punctuation. 27 .72/.73

English usage Knowledge of preferred phrasing in English. 25 .56/.49

Effective expression Items testing the ability to recognize whether an idea has been

expressed clearly, concisely and smoothly.

12 .63/.52

32

Word functions in

sentences

A test of sensitivity to grammatical structure. The test taker

must find the word that performs the same grammatical

function as a word in another sentence.

24 .81/.84

Reading

comprehension

Multiple-choice items on the comprehension of a written

passage.

48 .86/.84c

Creativity Verbal items requiring ingenious solutions to practical

problems.

20 .73/.68

Mechanical reasoning Items of the ability visualize the operation of physical force,

such as the effect of gravitation, gears, pulleys, levers, etc.

20 .76/.64

Visualization in 2

dimensions

Items requiring mental rotation of shapes in two dimensions. 24 .81/.80c

Visualization in 3

dimensions

Items on the ability to visualize a how a two-dimensional

figure would look after it were folded into a three-

dimensional one.

16 .70/.59

Abstract reasoning A non-verbal test on the ability to identify the logical

progression of elements in a complex pattern (similar to

Raven’s progressive matrices).

15 .66/.65

Math 1 Arithmetic

reasoning

A test of the ability to reason in the manner required to solve

arithmetic problems, with only very simple computation.

16 .73/.71

Math 2 Introductory

High School

mathematics

A test of mathematics taught up to an including the 9th

grade,

including items on algebra, fractions, simple geometry, etc.

24 .78/.73

Arithmetic

computation

A test of the speed and accuracy of addition, subtraction,

multiplication and division.

72 Not avail.

Table reading A test on the speed and accuracy of obtaining information

from a table.

72 Not avail.

Clerical checking A test on the speed and accuracy of checking whether two

pairs of names are identical.

74 Not avail.

Object inspection A test on the speed and accuracy of spotting small differences

between objects when comparing them visually.

40 Not avail.

Note: Descriptions adapted from Wise et al. (1979). Reliability estimates taken from Flanagan et al. (1964,

Table 2-5), and are based on the Kuder-Richardson Formula 21 (Kuder & Richardson, 1937) unless

otherwise noted. The reliabilities are lower-bound estimates, and are based on the mean for all grades

combined. a

Estimate based on Kuder-Richardson Formula 20. b

Estimate may be an overestimate due to lack of experimental independence of items (Flanagan et al.,

1964). c

Split-half reliability estimate.

3.2.3 Data preparation

Among the PT tests was a screening test consisting of basic knowledge

questions that were taught in elementary school; it was designed to identify students

33

who were functionally illiterate, mentally retarded, or who displayed an apathetic

attitude to the tests (Wise et al., 1979). A response credibility index was available

for all participants according to their scores on the screening test, taking into account

whether the score could be explained by illiteracy (a low score on the reading

comprehension test—R250), mental slowness (a low score on the clerical checking

test—A430), problems with clerical inaccuracy (a low percentage correct on the

clerical checking test—P430), or some combination of these. We removed cases

who scored below the threshold for the screening test, except those cases for which

no explanation was provided by their scores on the other three tests. Also, in order

not to restrict the range of cognitive ability, participants who failed the screening test

ostensibly due to mental slowness were left in the sample. Students with missing

scores on the screening test were also retained.

Prior to the analysis, data were screened for normality and outliers. Three

tests were found to have problematic violations of normality in each grade:

Capitalization and English Usage were found to be negatively skewed, and Table

Reading was positively skewed (all three also displayed leptokurtic distributions).

To deal with these violations, a logarithmic transformation was applied to

Capitalization, a square-root transformation to English Usage, and logarithmic and

cosine transformations were applied to Table Reading. The scores for Capitalization

and English usage were reflected prior to transformation, and re-reflected afterwards

in order to keep the original direction of the scores. The scores for Table Reading

were also re-reflected after transformation because the cosine transformation

reflected them. Following transformation, within each grade the highest remaining

skewness was for Clerical Checking (z = 0.70-1.08) and the highest remaining

kurtosis was for Table Reading (z = 1.74-2.63).

After transformation, no extreme univariate outliers remained given the large

sample size. In order to control for potential multivariate outliers the Mahalanobis

distance and Cook’s distance were obtained for complete cases (separately in the

broad and narrow selections). These statistics were obtained by regressing the PT

student ID number (a random variable) onto the test scores. Cases that had a

Mahalanobis distance with a p < .001 (χ2(37) = 69.35, for the broad selection, χ

2(22)

34

= 48.27, for the narrow selection), and a Cook’s distance of greater than 4/N were

removed from the sample (critical values suggested by Tabachnick & Fidell, 2007).

Following data screening, total sample size was reduced to 366,857 in the broad

selection, and 366,695 in the narrow selection (2.49 - 2.54 % of the sample

removed).

We handled missing data by using multiple imputation with five datasets for

the exploratory analyses and direct maximum likelihood estimation for the

confirmatory factor analyses. These missing-data methods yield the same results

(Brown, 2006), and require the assumption that the data be missing at random

(MAR). For this assumption to have been violated, students would have had

selectively to avoid particular tests specifically due to awareness of lower ability in

those areas. Given that 2.27-3.23% of all test scores were missing across each grade,

usually in relatively large ‘clumps’ for individuals, this is unlikely to have occurred

enough to affect the results. A comparison of means and correlations in the full

dataset to those with listwise deletion also showed only very small differences,

indicating that the pattern of results would be the same basically no matter how

missing data were treated.

3.2.4 Analysis method

Despite the existence of previous such analyses in PT, we used exploratory

factor analysis to estimate the factor structure given our particular selections of tests

and data screening methods (for example, our treatment of outliers and missing data).

Consistent with previous analyses of the PT data, factor analyses were performed

separately for each combination of grade and sex (Carroll, 1993; Reeve et al., 2006).

This was done to retain a number of replication samples and to identify possible

differences in the factor structures across the sexes and grades. In order to determine

the numbers of factors to extract in the exploratory analyses, parallel analysis and

Velicer’s minimum average partial (MAP) test were obtained for each sample in

SPSS (see O'Connor, 2000, for syntaxes); the Kaiser criterion (the number of

eigenvalues > 1) was also examined. Most important, however, was a consideration

of the interpretability of the factors and whether each factor had at least two tests

whose highest loadings were there (a criterion suggested by Carroll, 1993). All

35

analyses were performed with maximum likelihood estimation, and Promax rotation

(Kappa set to 4) was used for all the exploratory analyses.

For the CFA results, we report three conventional fit indices: the Root Mean

Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and

Standardized Root Mean Square Residual (SRMR) (Brown, 2006). An information

criterion index, the saBIC (sample size-adjusted BIC), was selected in order to be

able to directly compare our non-nested models. The saBIC was chosen because the

unadjusted BIC imposes a high penalty for additional parameters based upon sample

size (Kenny, 2011); the saBIC has also been found to perform well in model

selection (Sclove, 1987; Yang, 2006). Although not shown, AIC (Akaike

Information Criterion) statistics gave rise to the same conclusions as the saBIC.

3.3 Results

3.3.1 Exploratory factor analysis

3.3.1.1 Broad selection

For the males in the broad selection, parallel analysis and the MAP test each

indicated that there were four factors, while the Kaiser criterion suggested five

factors (except in grade 9 males, where it suggested four). However, when a sixth

factor was extracted it was clearly interpretable as the math knowledge factor that

was identified by previous researchers (Cureton, 1968; Carroll, 1993); the factor had

the highest loadings for Math Information and the second part of the Math test. In

contrast, a seventh factor was not clearly interpretable and contained no highest

loading from any test. Thus, we retained six factors in each male sample.

In the female samples, parallel analysis suggested there were only three

factors, but the MAP test indicated four factors in grades 9 and 10, and five in grades

11 and 12. The Kaiser criterion suggested five factors throughout. Nonetheless, the

sixth factor was identified as the Math factor in the same manner as in the male

samples. In grades 9 and 10 the seventh factors were singlets with the highest

loadings for Disguised Words and Memory for Sentences, respectively. In grades 11

and 12, the seventh factors had two highest loadings (Memory for Sentences and

Bible in grade 11, and Biological Sciences and Bible in grade 12), but these factors,

36

unlike the others, varied in each sample and were difficult to interpret; thus we

retained six factors in each female sample as well.

Table 3.3 presents the factor pattern matrices for grade 10 males and females,

with loadings under .15 suppressed (.15 was used as a cutoff to determine whether a

factor loading was substantively meaningful). Table 3.5 contains the factor

correlation matrices. The six factors were labelled in a manner similar to Cureton

(1968): the Information factor was formed largely by loadings from the information

tests. The English/Math factor had loadings from the English and Math achievement

tests. The Spatial/Reasoning factor was formed by tests with visual-spatial content,

but also tests that involved a reasoning component (such as Creativity, Math 1). The

Mechanical/Science factor had loadings from tests requiring knowledge in

mechanics, electronics and science subjects. The Speed factor was formed by

loadings from all the speeded tests in the battery. Finally, the Math factor had its

highest loadings from tests requiring math knowledge (as opposed to tests of

computation skills, which loaded more highly upon the English/Math factor).

37

Table 3.3

Factor pattern matrices for grade 10 males/females in the broad selection of PT tests.

Test Name Factor

Inform-

ation

English/

Math

Spatial/

Reasoning

Mechanical/

Science Speed Math

Vocabulary .55/.56 –/.16 .28/.20

Literature .93/.84

Music .75/.74

Social Studies .73/.62

Mathematics .26/.21 .54/.64

Physical Science .35/– .40/.52 .24/.16

Biological Science .41/.23 .31/.40

Aeronautics and Space .48/.33 .37/.33

Electronics .74/.68

Mechanics –/.18 .75/.55

Art .89/.90

Law .60/.55

Health .53/.45 .17/.29 .16/.17

Bible .69/.49

Theatre and Ballet .78/.88

Miscellaneous .65/.57

Memory for sentences .29/.36

Memory for words .17/.15 .34/.38

Disguised words .33/.37 .42/.43 .24/.25

Spelling .72/.81

Capitalization .63/.71

Punctuation .72/.76

English usage .62/.62

Effective expression –/.15 .57/.54

Word functions in sent. .42/.40 .16/.15 .31/.33

Reading comprehension .58/.54 .33/.33 .15/.15

Creativity .28/.29 .19/– .29/.32 .15/–

Mechanical reasoning .62/.69 .36/.15

Visualization in 2D .58/.59 .20/.18

Visualization in 3D .78/.77

Abstract reasoning .17/.25 .58/.57

Math 1 .38/.35 .18/.23 .27/.24

Math 2 .32/.26 .59/.62

Arithmetic comp. .52/.63 .30/.26 .26/.17

Table reading –/.17 .66/.64

Clerical checking .72/.72

Object inspection .30/.27 .62/.63

38

As seen in Table 3.3, the general pattern of loadings was highly similar across

the sexes, with some minor exceptions. The loadings for physical science, biology

and aeronautics tests on the Information factor were lower in females, which may be

attributable to their lower reliabilities in females (see Table 3.2). However, the

loadings of these tests on the Mechanical/Science factor were similar in each sex,

suggesting that they functioned equally well as tests of specific mechanical/science

knowledge in females as in males, but that they were better tests of general

knowledge for males than females. Another interesting sex difference was that the

test of mechanical knowledge loaded at .18 on the Spatial/Reasoning factor in

females, but below .15 in males. This suggests that mechanical knowledge was tied

more closely to spatial ability in females.

Differences in salient loadings across the eight samples were later used for

the construction of the confirmatory factor models. The differences were as follows:

Relative to grade 10 males, in grade 9 males the Vocabulary test had a

loading on English/Math, Reading Comprehension did not have a loading on

Spatial/Reasoning, Creativity did not load substantively (had a loading below .15)

onto the Mechanical/Science factor, and Punctuation had a loading on the Math

factor. In grade 11 males, Memory for Words did not load substantively on the

Information factor. In grade 12 males, the Vocabulary test had a loading on

English/Math and the Electricity and Electronics test had a loading on the Math

factor.

Relative to grade 10 males, in grade 10 females Effective Expression loaded

on the Information factor, while Physical Science Information did not5, Vocabulary

and Table Reading loaded on English/Math, while Creativity did not, and

Mechanical Information loaded on Spatial/Reasoning.

In grade 9 females, relative to grade 10 females, Memory for Words and

Effective Expression did not load on Information, Bible had a loading on

5 This was a logical finding given that most students were probably not exposed to formal Physics

classes until higher grade levels. However, unlike in the female samples, in grade 9 and 10 males

Physical Science did load on Information, perhaps because boys were more likely to be exposed to

Physics knowledge outside of school.

39

English/Math, Word Functions in Sentences and Reading Comprehension did not

load on Spatial/Reasoning, Mechanical reasoning did not load on the

Mechanical/Science factor, and Social Science information loaded on the Math factor

but Physical Science did not.

In grade 11 females, relative to grade 10 females, Physical Science and

English Usage loaded on Information, Table Reading did not load on English/Math,

and Health information did not have a loading on Mechanical/Science.

Finally, in grade 12 females, relative to grade 10 females, Physical Science

and English Usage loaded on Information, while Memory for Words did not,

Vocabulary and Table Reading did not load on English/Math, and Vocabulary and

Health information did not load on Mechanical/Science.

3.3.1.2 Narrow selection

In males, parallel analysis and the MAP test indicated three factors. The

Kaiser criterion suggested three factors in grades 9 and 10, and four factors in grades

11 and 12. In females, all the criteria suggested three factors. Nonetheless, five

factors were retained for both sexes because the fourth and fifth factors corresponded

to factors in the broad selection, and contained the highest loadings from at least two

tests.

The sixth factor was not retained for multiple reasons. In each male sample

the sixth factor was a singlet with Memory for Sentences, and thus was clearly not

interpretable. In grade 9 females, there were no highest loadings on the sixth factor;

in grade 10 there was a doublet with Memory for Words and Memory for Sentences,

and in grade 11 and 12 females there was once again a singlet with Memory for

Sentences. Although there was some evidence for a Memory factor in females, we

considered that the relation between the memory tests would be best handled with

correlated error variances instead of a factor, because of the similarity in the format

of the tests (memorization of words or sentences, followed by multiple-choice items

testing recall).

Table 3.4 displays the factor pattern matrices for grade 10 males and females,

and Table 3.5 the factor correlations. Four of the factors were highly similar to those

40

in the broad selection and were labelled the same: English/Math, Spatial/Reasoning,

Speed and Math. Due to the removal of the science and mechanical information

tests, there was no longer a factor for them. The fifth factor had the highest loadings

for the Vocabulary and Creativity tests and was labelled the Verbal factor because all

its loadings came from tests with verbal subject-matter content.

Table 3.4 Factor pattern matrices for grade 10 males/females in the narrow selection of PT tests.

Test Name Factor

English/Math

Spatial/ Reasoning Speed Math Verbal

Vocabulary .36/.43 .50/.48

Memory for sentences .18/.23

Memory for words .38/.38 –/.15

Disguised words .58/.60 .26/.27 .27/.24

Spelling .79/.81

Capitalization .69/.71

Punctuation .81/.77

English usage .71/.72

Effective expression .60/.59

Word functions in sent. .44/.40 .29/.30

Reading comprehension .51/.52 .45/.44

Creativity .22/.20 .24/.24 .40/.37

Mechanical reasoning .72/.66 .18/–

Visualization in 2D .60/.60 .20/.17

Visualization in 3D .78/.76

Abstract reasoning .21/.28 .54/.53

Math 1 .21/.24 –/.16 .43/.38 .15/.16

Math 2 .17/.23 .68/.58

Arithmetic comp. .28/.38 .30/.25 .44/.39

Table reading .66/.66

Clerical checking .73/.73

Object inspection .28/.26 .62/.63

Relative to grade 10 males, the only factor loading differences in males were

that Math 1 loaded on Spatial/Reasoning in grades 11 and 12.

Relative to grade 10 males, in grade 10 females, Math 1 had a loading on

Spatial/Reasoning, Memory for words had a loading on Verbal, and Mechanical

reasoning did not load on the Verbal factor.

41

In grade 9 females, relative to grade 10 females, Memory for Words had a

loading on the Math factor (allowed only in the VPR model), and Memory for Words

and Math 1 did not load on Verbal.

In grade 11 and grade 12 females, relative to grade 10 females, Math 2 did

not load on English/Math, and Memory for Words and Math 1 did not load on

Verbal.

Table 3.5 Factor correlation matrices for grade 10 males (below diagonal) and females (above diagonal)

in the broad and narrow selections of PT tests.

Info.

English/

Math Spatial/ Reasoning Math Speed

Mech./ Science

Information – .783 .659 .641 .093 .722

English/Math .760 – .676 .688 .196 .570

Spatial/Reas. .593 .593 – .571 .186 .602

Math .612 .607 .500 – .064 .614

Speed .099 .201 .140 .086 – .008

Mech./Science .692 .482 .642 .504 -.037 –

Narrow Selection

Verbal – .582 .595 .513 .070

English/Math .590 – .626 .694 .267

Spatial/Reas. .597 .561 – .593 .294

Math .494 .737 .577 – .175

Speed .068 .211 .163 .176 –

3.3.2 Confirmatory factor analyses

3.3.2.1 Broad selection

Based upon the results of the exploratory analysis, we developed

confirmatory models for the VPR, CHC and Gf-Gc models (see Figures 3.1-3.3).

Table 3.6 displays the loadings of the tests on the first-order factors for the grade 10

male sample. Differences in factor loadings compared to this sample were noted

above in section 3.1.1. Although the number of factor loadings varies across the

models, the number of input variables was the same for each. Supplemental tables

A1 and A2 provide the numerical first-order loadings for the grade 10 males and

females for all models (see Appendix A).

42

In order to represent the three models under consideration accurately, factor

loadings were only included if they were consistent with the theories behind the three

models, taking into account whether test content could account for any indicated

cross-loadings. For the Gf-Gc and CHC models, for example, there was no

theoretical or content rationale for the math achievement and arithmetic tests to load

onto a factor otherwise dominated by English tests (these factors were characterized

as English achievement in the CHC model (following Carroll, 1993)6, and Verbal

comprehension in the Gf-Gc model). A hybrid first-order English/Math factor is also

not specified in the models, and the first-order factors for quantitative ability and

English achievement are theorized to load onto separate second-order factors in both

(Horn & Blankson, 2005; McGrew, 2005). In contrast, a factor combining English

and Math tests is theoretically plausible within the VPR model because factors

formed by these tests are in the same domain, underneath the broad Verbal (formerly

verbal-educational) factor (Johnson & Bouchard, 2005b), and because the VPR

model does not pre-specify factor content. Additionally, three verbal tests were

found to load onto the Math factor (Word Functions in Sentences, Memory for

Words, and Punctuation). These cross-loadings were included in the VPR model

because of the theoretically-based connection between the Math and English factors,

but were not allowed in the CHC and Gf-Gc models.

6 See dataset SHAY01 in Carroll (1993). This factor was also characterized as English language

usage in the other PT datasets, but the label of English achievement seemed more appropriate to us.

43

Table 3.6

First-order loadings for the CHC, Extended Gf-Gc and VPR models in the broad selection (grade 10 males).

Test Primary loadings Secondary loading(s)

CHCa

Gf-Gcb

VPR CHC Gf-Gc VPR

Vocabulary K0 Vi Information K1 Science Mechanical/Science

Literature K0 Vi Information

Music K0 Vi Information

Social Studies K0 Vi Information

Mathematics KM Gq Math K0 Vi Information

Physical Science K1 Science Mechanical/Science K0, KM Vi, Gq Information, Math

Biological Science K0 Vi Information K1 Science Mechanical/Science

Aeronautics and Space K0 Vi Information K1 Science Mechanical/Science

Electronics K1 Science Mechanical/Science

Mechanics K1 Science Mechanical/Science

Art K0 Vi Information

Law K0 Vi Information

Health K0 Vi Information A6, K1 V, Science English/Math,

Mechanical/Science

Bible K0 Vi Information

Theatre and Ballet K0 Vi Information

Miscellaneous K0 Vi Information

Memory for sentences A6 V English/Math

Memory for words A6 V English/Math K0 Vi Information

Disguised words A6 V English/Math K0, P Vi, P Information, Speed

Spelling A6 V English/Math

Capitalization A6 V English/Math

Punctuation A6 V English/Math

English usage A6 V English/Math

Effective expression A6 V English/Math

Word functions in sent. A6 V English/Math Vz Visualization Math,

Spatial/Reasoning

Reading comprehension K0 Vi Information A6, Vz V, Visualization English/Math,

Spatial/Reasoning

Creativity Vz Visualization Spatial/Reasoning K0, A6, K1 Vi, V, Science Information,

English/Math,

44

Mechanical/Science

Mechanical reasoning Vz Visualization Spatial/Reasoning K1 Science Mechanical/Science

Visualization in 2D Vz Visualization Spatial/Reasoning P P Speed

Visualization in 3D Vz Visualization Spatial/Reasoning

Abstract reasoning Vz Visualization Spatial/Reasoning A6 V English/Math

Math 1 KM Gq English/Math Vz Visualization Math,

Spatial/Reasoning

Math 2 KM Gq Math A6 V English/Math

Arithmetic comp. KM Gq English/Math P P Speed, Math

Table reading P P Speed

Clerical checking P P Speed

Object inspection P P Speed Vz Visualization Spatial/Reasoning a K0 = general verbal information, K1 = science knowledge, A6 = English achievement, KM = math achievement, P = perceptual speed.

b Vi = general

information, V = verbal comprehension, Gq = quantitative knowledge.

45

Fig. 3.1. Measurement model of the VPR model with factor loadings from the grade 10 male sample.

46

Fig. 3.2. Measurement model of the Extended Fluid-Crystallized model with factor

loadings from the grade 10 male sample.

Fig. 3.3. Measurement model of the Cattell-Horn-Carroll model with factor loadings

from the grade 10 male sample.

47

According to the CHC framework, the six first-order factors that we obtained each

belonged underneath separate second-order factors (McGrew, 2009). Unfortunately,

these second-order factors could not be identified with only one loading, thus the

CHC model here effectively consisted only of first-order factors loading upon a

second-order g factor. The names of the second-order factors are included in Figure

3.3 only for illustration purposes as they contributed nothing to the estimation of the

model.

In the course of fitting the VPR model, a negative residual was encountered

for the second-order Verbal factor, which was formed from the first-order factors

Information, English/Math and Math (in males z = -9.08 to -5.92, p < .001; in

females this residual was either negative or non-significant, z = -1.48 to 5.80). This

suggested that too much of the test variance was being assigned to the Verbal factor,

possibly because of the large number of tests forming the factors composing it (see

Figure 3.1). In order to resolve this issue, the Information factor was placed on its

own second-order factor (also named the Information factor), which eliminated the

negative residual and improved model fit. Based on the exploratory analysis it was

apparent that the first-order Speed factor, which consisted of mainly clerical-type

speed tests, might load onto the Verbal factor formed by the Math and English tests;

the first-order Speed factor’s highest correlation was with the English/Math factor

(see Table 3.5). Vernon (1961) observed that factors formed by clerical-type tests

often loaded within the verbal-educational domain. As predicted, placing the Speed

factor on the Verbal factor improved the fit of the model compared to the Speed

factor having a separate loading on g, and this model was used as the final version of

the VPR model.

The VPR, CHC and Gf-Gc models fit well in both males and females samples

according to conventional fit criteria (Hu & Bentler, 1999): the RMSEA was below

.060 in all cases, the SRMR below .080, and the CFI was close to .950 or greater

(Table 3.7). The VPR model demonstrated the best fit in all the samples according to

all fit indices that were used, followed primarily by the CHC model. A BIC

difference of 10 is normally considered very strong evidence in favour of the model

48

with the smaller value (A. E. Raftery, 1995); however, because of the large PT

sample sizes the saBIC values for our models were so large that a difference of 10

was trivial. Therefore, we calculated what the saBIC differences would have been if

the samples were a more conventional 500.7 As shown in Table 3.7, the VPR model

had the lowest saBIC at a sample size of 500 compared with the CHC and Gf-Gc

models in each sample. In males, the VPR had a saBIC 55.0 – 93.6 lower than the

CHC model, and 61.9 – 100.3 lower than the Gf-Gc model. In females, the VPR had

a saBIC 36.9 – 119.3 lower than the CHC model, and 43.4 – 115.10 lower than the

Gf-Gc model. The VPR model had the lowest saBIC despite being the least

parsimonious model (it containined the highest number of freely-estimated

parameters). Thus, the VPR model was found to consistently have the best fit to the

broad selection of PT tests. The CHC model had a lower saBIC than the Gf-Gc

model in five out of eight samples, but the saBIC difference was lower than 10 in all

cases, thus in general the difference in fit between these models was marginal.

7 The formula for the saBIC is -2(log-likelihood) + pln[(N +2)/24], where p is the number of freely-

estimated parameters. Since log-likelihood is linearly related to sample size, it was scaled by the ratio

of 500 to the full sample size for each sample. In the second half of the equation, 500 was entered for

N.

49

Table 3.7

Fit statistics of confirmatory factor models for the broad selection of PT tests.

Sample Sample

size

χ2 df saBIC RMSEA

(95% CI)

CFI SRMR saBIC,

sample

of 500

Males

VPR model

Grade 9 49264 45592.64 592 8755769.82 .039 (.039-.040) .955 .036 89301.38

Grade 10 48561 51421.27 593 8761767.73 .042 (.042-.042) .951 .037 90652.44

Grade 11 44172 51480.47 594 8015636.24 .044 (.044-.044) .949 .036 91163.59

Grade 12 38894 47517.10 592 7040873.99 .045 (.045-.045) .948 .036 90949.57

Cattell-Horn-Carroll Model

Grade 9 49264 52812.82 599 8762936.61 .042 (.042-.042) .948 .039 89356.34

Grade 10 48561 59600.54 599 8769901.32 .045 (.045-.045) .943 .040 90715.45

Grade 11 44172 61253.79 600 8025364.45 .048 (.048-.048) .939 .040 91255.98

Grade 12 38894 56212.83 598 7049525.37 .049 (.049-.049) .938 .041 91043.11

Fluid-Crystallized model

Grade 9 49264 52597.02 596 8762743.69 .042 (.042-.042) .948 .038 89363.28

Grade 10 48561 59357.72 596 8769681.34 .045 (.045-.045) .943 .040 90722.07

Grade 11 44172 60878.92 597 8025012.13 .048 (.047-.048) .939 .040 91263.89

Grade 12 38894 55655.29 595 7048990.01 .049 (.048-.049) .939 .040 91045.06

Females

VPR model

Grade 9 49973 36559.98 595 8863188.52 .035 (.034-.035) .962 .025 90385.69

Grade 10 48237 37640.05 591 8598498.30 .036 (.036-.036) .961 .025 88974.32

Grade 11 46504 40765.08 591 8329464.47 .038 (.038-.039) .958 .032 94724.80

Grade 12 41119 42975.16 594 7378914.32 .042 (.041-.042) .951 .042 95289.34

Cattell-Horn-Carroll Model

Grade 9 49973 41993.83 601 8868576.52 .037 (.037-.037) .956 .026 90422.60

Grade 10 48237 44907.56 597 8605720.18 .039 (.039-.040) .954 .027 89030.91

Grade 11 46504 50429.74 597 8339083.71 .042 (.042-.043) .948 .034 94815.95

Grade 12 41119 53673.68 600 7389568.16 .046 (.046-.047) .938 .037 95408.62

Fluid-Crystallized model

Grade 9 49973 41739.25 598 8868344.86 .037 (.037-.037) .956 .026 90429.14

Grade 10 48237 44481.76 594 8605317.19 .039 (.039-.039) .954 .026 89035.65

Grade 11 46504 49579.24 594 8338255.94 .042 (.042-.042) .949 .033 94815.45

Grade 12 41119 52638.67 597 7388555.49 .046 (.046-.046) .939 .036 95404.44

3.3.2.2 Narrow selection

Confirmatory factor models for the narrow selection of tests were developed

in the same manner as those for the broad selection, based upon the exploratory

analysis (see Table 3.8 for the first-order loadings; differences in the loadings across

each sample were detailed in 3.1.2.). Once again cross-loadings for Math tests onto

the English achievement (in the CHC model) and Verbal comprehension factor (in

the Gf-Gc model) were fixed to zero, as was the loading of Word Functions in

Sentences on their Math factors.

50

The initial VPR model fit properly in all grades except in grade 9 males,

where a small negative residual was encountered for the Math factor (standardized

loading = -.034). Fixing this residual to zero resulted in another negative residual for

the Math 2 test, and a negative loading for this test on the English/Math factor

(standardized loading = - .35), indicating that this factor loading was the source of

model misfit. Thus, the Math 2 loading was removed from English/Math, which

resolved the negative residual.

The second-order structures of the models were the same as in the broad

selection, except the absence of a first-order Mechanical/Science factor meant that

there was no longer a second loading onto the Perceptual factor in the VPR model or

a third loading on the Gc factor in the Gf-Gc model. In the CHC model, the second-

order factor for science knowledge (K1) was absent. The Verbal factor occupied the

same role as the Information factor did in the broad selection.

Table 3.9 contains the fit statistics for the models based on the narrow

selection. The VPR model again fit the data best according to all criteria (except for

two comparisons where the SRMRs were equal). The second best-fitting model was

generally the CHC model, but the saBIC differences between the CHC model and

Gf-Gc model were again small and inconsistent, pointing to only a marginal

difference overall between them. In the hypothetical male samples of 500, the VPR

had a saBIC 8.4 – 17.5 lower than the CHC model, and 14.8 – 24.6 lower than the

Gf-Gc model. In females, the VPR had a saBIC 15.8 – 34.4 lower than the CHC

model, and 21.3 – 30.6 lower than the Gf-Gc model. Although the saBIC difference

between the VPR and CHC model was less than 10 in grade 9 males, a difference of

8.4 is still characterized as “strong” evidence of a model fit difference, and

corresponds to a posterior odds of 66:1 in favor of the VPR model (Raftery, 1995).

The AIC, RMSEA and CFI also favored the VPR over the CHC model. Thus, the

VPR model demonstrated the overall best fit to the narrow selection of PT tests.

51

Table 3.8 First-order loadings for the CHC, Extended Gf-Gc and VPR models in the narrow selection (grade 10 males). Test Primary loadings Secondary loading(s)

CHCa

Gf-Gcb

VPR CHC Gf-Gc VPR

Vocabulary K0 Vi Verbal A6 V English/Math Memory for sentences A6 V English/Math Memory for words A6 V English/Math Disguised words A6 V English/Math K0, P Vi,P Verbal, Speed Spelling A6 V English/Math Capitalization A6 V English/Math Punctuation A6 V English/Math English usage A6 V English/Math Effective expression A6 V English/Math Word functions in sent. A6 V English/Math Math

Reading comprehension A6 V English/Math K0 Vi Verbal Creativity K0 Vi Verbal Vz,

A6 Visualization, V

Spatial/Reasoning, English/Math

Mechanical reasoning Vz Visualization Spatial/Reasoning K0 Vi Verbal Visualization in 2D Vz Visualization Spatial/Reasoning Visualization in 3D Vz Visualization Spatial/Reasoning Abstract reasoning Vz Visualization Spatial/Reasoning A6 V English/Math Math 1 KM Gq Math P P English/Math, Speed Math 2 KM Gq Math English/Math

Arithmetic comp. KM Gq Math P P Speed, English/Math Table reading P P Speed Clerical checking P P Speed Object inspection P P Speed Vz Visualization Spatial/Reasoning

a K0 = general verbal information, A6 = English achievement, KM = math achievement, P = perceptual speed.

b Vi = general information, V = verbal comprehension, Gq = quantitative knowledge.

52

Table 3.9

Fit statistics of confirmatory factor models for the narrow selection of PT tests.

Sample Sample

size

χ2 df saBIC RMSEA

(95% CI)

CFI SR

MR

saBIC in

sample of

500

Males

VPR model

Grade 9 49096 18297.50 186 5724268.66 .045(.044-.045) .962 .041 58560.39

Grade 10 48254 18867.29 185 5743450.08 .046(.045-.046) .962 .040 59448.13

Grade 11 44165 17562.76 184 5254414.22 .046(.046-.047) .963 .037 59755.12

Grade 12 38877 16526.62 184 4611559.86 .048(.047-.048) .962 .037 59577.66

Cattell-Horn-Carroll Model

Grade 9 49096 20313.12 190 5726253.80 .046(.046-.047) .958 .041 58568.75

Grade 10 48254 21446.22 190 5745990.95 .048(.047-.049) .957 .041 59459.50

Grade 11 44165 20453.84 189 5257267.72 .049(.049-.050) .957 .039 59772.65

Grade 12 38877 18794.95 189 4613791.25 .050(.050-.051) .956 .039 59591.63

Fluid-Crystallized model

Grade 9 49096 20045.70 187 5726009.25 .047(.046-.047) .959 .041 58575.15

Grade 10 48254 21272.96 187 5745840.53 .048(.048-.049) .957 .041 59466.84

Grade 11 44165 20276.00 186 5257112.43 .049(.049-.050) .957 .038 59779.76

Grade 12 38877 18555.20 186 4613573.67 .050(.050-.051) .957 .038 59597.67

Females

VPR model

Grade 9 49922 16921.93 185 5815009.26 .043(.042-.042) .967 .037 59487.47

Grade 10 48209 16689.62 184 5648320.43 .043(.043-.044) .968 .036 58470.86

Grade 11 46499 17314.84 187 5468164.36 .044(.044-.045) .965 .035 62166.16

Grade 12 41095 16920.67 187 4830928.59 .046(.046-.047) .961 .034 62390.14

Cattell-Horn-Carroll Model

Grade 9 49922 20269.31 191 5818310.80 .045(.045-.046) .960 .038 59503.31

Grade 10 48209 19909.80 189 5651502.59 .047(.046-.047) .961 .038 58488.84

Grade 11 46499 21025.72 191 5471844.96 .048(.048-.049) .958 .037 62196.01

Grade 12 41095 21537.53 191 4834515.66 .051(.050-.052) .953 .037 62424.49

Fluid-Crystallized model

Grade 9 49922 19912.12 188 5817976.53 .046(.045-.046) .961 .038 59508.80

Grade 10 48209 19487.37 186 5651102.98 .046(.046-.047) .962 .037 58493.61

Grade 11 46499 20282.65 188 5471124.61 .048(.047-.048) .959 .035 62196.72

Grade 12 41095 18647.02 188 4832647.49 .049(.048-.049) .957 .033 62409.30

3.3 Discussion

We found that the VPR model provided the best fit to the Project TALENT

battery of the three models that were tested. Three studies now support the

conclusion that the VPR model provides a better description of the structure of

intelligence than the CHC model or its precursor the three-stratum model (Johnson &

Bouchard, 2005b, Johnson, te Nijenhuis, & Bouchard, 2007), and four studies now

support the VPR model over the Gf-Gc model (Johnson & Bouchard, 2005a, 2005b;

53

Johnson, Te Nijenhuis, et al., 2007). No study to date has contradicted this

conclusion.

Nonetheless, the PT dataset had some limitations for testing the structure of

intelligence differences. As always, test selection was of relevance to how the

different theoretical models were represented. The PT test battery was not suited to

testing all of the differences between the models because it lacked tests in certain

domains such as long-term storage and retrieval (Glr) and reaction and decision

speed (Gt) (McGrew, 2009). However, in its favor, the PT battery was not

constructed according to any particular theory of intelligence, and, as we argue

below, it was suitable for testing each model’s predictions in mechanical/scientific

and verbal/educational domains. A second potential limitation of our findings is that

even though the sample was representative of American high school students in the

early 1960s, the pattern of results may not generalize to more recent samples due to

cultural and educational changes. The structure of ability may also shift with age,

becoming more differentiated as students have the opportunity to learn more

specialized knowledge and subsequently enter the workforce as adults (Li et al.,

2004).In this section, we first discuss some possible reasons why the VPR theory

outperformed CHC/Gf-Gc theory in explaining the structure of the PT battery.

Second, we describe some differences in how the VPR model was manifested in PT

compared to previous studies. Third, we turn to the theoretical implications of this

study and previous comparison studies of the structure of intelligence.

3.3.1 The three theories in Project TALENT

The VPR model described the overall structure of the PT battery more

accurately than the CHC and Gf-Gc models. Yet one possible disadvantage of the

battery for the CHC model may have been that it effectively lacked second-order

factors (such as Gc, Grw, etc.; see Figure 3.3) because there were not enough

indicators for them. Nevertheless, CHC theory dictates that the six first-order factors

we observed all load onto different second-order factors; thus the creation of second-

order factor that were combinations of different first-order factors would not have

been consistent with the theory (McGrew, 2009). In addition, such a model with

only one higher-order factor received support from Carroll (1993), who found this

54

structure in the male-only PT datasets SHAY01 and FLAN01. Such a model has

been used as representative for CHC theory in PT by later researchers (Reeve, 2004;

Reeve et al., 2006) Although Carroll (1993) found three second-order factors in the

female-only dataset SHAY02 (see Tables 15.4 and 15.14, Carroll, 1993), two of

these factors were inconsistent with both his three-stratum theory and the more

recent CHC theory. These factors were the 2H factor (the combination of fluid and

crystallized intelligence), which is lacking in both models, as is the Technical

Information factor that received loadings from the General Information factor (K0),

Math knowledge (KM) and General Science Information (K1). Thus, our version of

the CHC model was consistent with the structure Carroll found in the male PT

datasets, and was true to the most recent description of the CHC model in terms of

the classification of first-order factors as loading onto particular second-order factors

(McGrew, 2009). Nevertheless, our results contradicted the prediction derived from

CHC theory that the six first-order factors in PT belong to six different domains (and

thus make six independent contributions to g; see Figure 3.3). Similarly, our results

also contradicted the four second-order factors of the Extended Gf-Gc model:

crystallized intelligence (Gc), visualization (Gv), quantitative abilities (Gq) and

cognitive speed (Gs); see Figure 3.2. Some of the second-order factors of the Gf-Gc

and CHC models were likely not fully representative of the broad abilities in the

models due to the relative narrowness of the PT test battery in certain domains (such

Gs or Gq), but if the structure of ability were divided into those domains, rather than

those predicted by the VPR model, then the models should still have demonstrated

better fit than the VPR model. These same arguments can also be extended to the

three models in the narrow selection of tests.

As noted above, we believe that, despite limitations for testing these theories

in terms of the test battery content, PT was suitable for testing their predictions about

tests in the mechanical/scientific and verbal/educational domains. An important area

where the VPR model and CHC/Gf-Gc models differ is in their conceptualizations of

where tests of mechanical/science knowledge fit in the structure of intelligence. The

VPR model predicts that performance on these tests depends on the underlying

Perceptual and/or Image Rotation abilities and skills of the test taker, as well as

interest in and experience with the subject area. Thus the VPR model specifies that

55

tests of mechanics and science information are distinct from measures of acculturated

knowledge that rely more directly on verbally expressed knowledge (e.g. General

Information). The Gf-Gc model does not make this distinction and instead holds that

all measures of acquired knowledge are part of crystallized intelligence (Gc) as

distinct from non-learned reasoning and information processing capacities. Although

the CHC model does distinguish factors of domain-specific knowledge from Gc, it

does not place specific mechanical/science knowledge with spatial ability as the VPR

model does. The PT battery provided a difficult test for the VPR model because the

mechanical and scientific information tests were based entirely on the recall of stored

knowledge, and the Spatial/Reasoning factor was made up of tests that were nearly

all visual-spatial and based on novel problem-solving. The results of the current

study thus provide support for the VPR view that spatial ability and

mechanical/science knowledge are linked under Perceptual ability.

Another area where the models differ is on how tests of English and Math

ability fit into the intelligence hierarchy. Both the Gf-Gc and CHC models dictate

that Quantitative abilities and English language abilities load onto separate second-

order factors (Horn & Blankson, 2005; McGrew, 2009). The VPR model, in

contrast, proposes that Math and English abilities are linked under the broad Verbal

factor (though Math abilities also often load on the Perceptual factor, see Johnson &

Bouchard, 2005b). Thus, the VPR model can also explain why a first-order

English/Math factor was obtained in the PT battery, but the Gf-Gc and CHC model

cannot.

Despite favoring the VPR model, the difference in fit according to the CFI

and SRMR were not large, particularly for the narrow selection. The interpretation

of the VPR as better fitting was dependent on the validity of a handful of cross-

loadings. It is possible that proponents of the Gf-Gc and CHC theories could

propose alternative better-fitting specifications of their models in Project TALENT.

The VPR model also benefitted from a more flexible theoretical stance (see section

3.3.3 for further discussion).

In summary, the better fit of the VPR model may be explained because it

made allowance for the underlying relation of the Spatial ability and

56

Mechanical/Science factors in the form of the second-order Perceptual factor, and the

relation between the English and Math factors in form of the second-order Verbal

factor. The lack of these factors in the CHC/Gf-Gc models and their too precise

specification of second-order factors likely explain their poorer fits to the data.

3.3.2 Variations in VPR model specifications

The specifications of the VPR model in the studies that have fit it have

varied. One notable difference in the PT VPR models from previous ones was that

model fit was improved when the Information factor was separated from the broad

second-order Verbal factor. Previous studies have only found only one Verbal factor

in the VPR model (Johnson & Bouchard, 2005a, 2005b; Johnson, Te Nijenhuis, et

al., 2007). Separation of the two factors may have been helpful because there was a

surplus of Information and English/Math-related tests in this school-oriented battery,

creating a spuriously high correlation between g and the Verbal factor with the

Information loading upon it. This suggests that these two factors were bloated

specific (overrepresentation of tests in closely related subject areas), as indicated by

the high number of tests loading upon them relative to the other factors (see Tables

3.5 and 3.8). At the same time, the finding of more than one second-order Verbal

factor is not inconsistent with VPR theory, as, like Vernon (1961), it proposes that

the number of factors at any stratum and the precise borders between them are

functions of the specificity of the tests in the battery rather than inherent facts of

nature (see section 3.3. below for further discussion).

In the first study on the VPR model, the first-order Perceptual Speed factor

was subsumed under the second-order Perceptual factor (Johnson & Bouchard,

2005b). Here, however, the first-order Speed factor was found to correlate highest

with the English/Math factor, and thus was placed under the Verbal factor. These

findings support Vernon’s (1961) view that speed factors can be subsumed by either

of the Verbal or Perceptual factors, depending on the overall battery test content. In

the PT test battery, the Speed factor was largely made up of tests with

verbal/numerical content (e.g. Table Reading, Clerical Checking, Disguised Words).

This factor can thus be compared more closely to the Fluency factor found in the

operationalization of the VPR model in the Thurstone and Thurstone (1941) test

57

battery (Johnson and Bouchard, 2005b). In the VPR model specified in Wolff and

Buiten's (1963)’s battery, in contrast, the Speed factor was more perceptual in nature,

involving more visual than verbal/numerical stimuli (Johnson, te Nijenhuis, &

Bouchard, 2007). Despite its loading on the Verbal factor, the Speed factor in the

current study did potentially also have a Perceptual component, as illustrated by the

cross-loading of the Object Inspection test on the Spatial/Reasoning factor (see

Tables 3.2 and 3.3). Thus, different factors that are each labelled as speed across

studies may have quite different characteristics depending on the content of the tests

involved in the factor.

The factors underlying the Perceptual factor in the PT battery can be

compared most closely with those obtained in the analysis of de Wolff and Buiten

(1963)’s test battery (Johnson, te Nijenhuis & Bouchard, 2007). In that battery, a

first-order factor of mechanical reasoning was formed from tests of knowledge about

tools and reasoning using mechanical principles, which is comparable to the

Mechanical/Science factor we obtained in the broad selection of PT tests. Similar

factors of mechanical reasoning were not identified in the two other studies on the

VPR model because these test batteries lacked tests in that domain, with the

exception of the mechanical ability test in the Minnesota Study of Twins Reared

Apart (MISTRA) battery, which loaded onto the spatial factor (Johnson & Bouchard,

2005b). A second similarity with the Perceptual factor in de Wolff and Buiten

(1963)’s battery is that its highest loading was from a factor of inductive reasoning

formed mainly by matrix reasoning tests, which is similar to the Spatial/Reasoning

factor of the current study. One remaining difference between the VPR model in the

PT and previous studies is the lack of a separate Image Rotation factor, but this can

be explained by the deficiency of pure tests for this ability in the PT battery aside

from the Visualization in Three Dimensions test.

3.3.3 Theoretical implications for the structure of intelligence

An important aspect of VPR theory that distinguishes it from other theories of

intelligence is the proposal that the main dimension, after g, along which the

structure of intelligence is organized is the Verbal-Perceptual-Image Rotation

dimension. Johnson and Bouchard (2005a) postulated that this dimension reflects

58

the fact that Verbal abilities rely on the serial, analytic functions of the left brain

hemisphere, while Perceptual and Image Rotation abilities rely more on the non-

verbal, parallel functioning of the right hemisphere (see also Gustafsson, 1984). In

further support of this view, Johnson and Bouchard (2007) found that when g was

partialed from from the 42-test MISTRA battery, there was a residual dimension with

Verbal abilities on one pole and Image Rotation and Perceptual abilities on the other.

This dimension also displayed a strong sex difference. Further research has

suggested that position on the Verbal-Image Rotation dimension is associated with

regional brain differences (Johnson, Jung, Colom, & Haier, 2008).

McGrew (2009) observed that while the CHC framework has already

had a strong influence on applied fields, “the adoption of the CHC umbrella term has

been much slower in theoretical fields, such as research published in the journal

Intelligence.” (p. 3). One possible reason that basic researchers have been slow to

embrace it is that, in contrast with VPR theory, CHC theory lacks the theoretical

content and predictions to satisfy researchers’ requirements for a basic theory of

intelligence. Carroll (1993) stated that a theory about the structure of cognitive

abilities should “provide hypotheses about the sources of individual differences in

these abilities” (p. 631); however, he proposed or tested few such hypotheses for his

three-stratum theory, and subsequent CHC researchers, as well as Extended Gf-Gc

researchers, have seemed satisfied to produce ever-growing catalogues of ability

factors, without substantial theoretical investigation into the cognitive, genetic or

neurological underpinnings of these new factors (McGrew, 2009; Horn & Blankson,

2005).

One strong theoretical prediction of VPR theory, derived from its dimension

view of intelligence, is that any attempt to specify the number of factors at any order

or stratum is inappropriate because the dimension is continuous. The implication of

this view, which is different from the discrete-factors view of the CHC and Gf-Gc

theories, is that there are many points along the Verbal-Image Rotation dimension

where factors could be found if appropriate tests were used. Thus, the only

prediction for the content of factors is that they lie somewhere along this dimension,

with their specific character dependent on the content of the tests in the battery.

59

Nonetheless, VPR theory would not predict, for example, that a factor would be

found which combines Verbal and Image Rotation tests, because these represent

positions that are far apart on the dimension. Another related prediction of VPR

theory is that even the number of strata is dependent on the level of detail of

measurement, and thus the number of strata could potentially be increased

indefinitely with tests of sufficient breadth and detail. These theoretical points of

view contrast with those in the Gf-Gc and CHC theories that the structure of

intelligence is organized into a limited number of identifiable factors and strata.

Another important theoretical difference is that in the CHC and Gf-Gc

models the broad second-order factors are conceptualized to depend more on either

purportedly novel processing abilities or acquired knowledge (Carroll, 1993;

McGrew, 2009; Horn & Blankson, 2005), whereas the VPR model maintains that

Verbal, Perceptual and Image Rotations abilities all involve stored knowledge, which

includes knowledge about how to approach problems involving freshly-generated

processing, with the balance depending on the particular constructions of the tests

and the experiences and knowledge of the test takers. For example, the Perceptual

factor in the PT VPR model is incongruent with CHC and Gf-Gc theory because it

received loadings from a factor made up from information tests

(Mechanical/Science), and a factor made up of (often-presumed novel) reasoning

tests (Spatial/Reasoning), but VPR theory holds that these factors are linked because

of their reliance on similar spatial/mechanical content and underlying perceptual

ability. This concept seems to have been overlooked in the literature, and in test

design, because the distinction between crystallized and fluid factors is often

confounded with the division between tests involving verbal and spatial content.

Carroll (1993)’s classifications are also subject to this criticism. Of the factors

classified as Gf in his review, the three most frequent first-order factors that had high

loadings on Gf were Induction, Visualization and Sequential Reasoning (Carroll,

1993, p. 598). Induction was defined most often by Ravens Progressive Matrices,

Visualization was made up entirely of loadings from visual-spatial tests, and

Sequential Reasoning was defined most often by a test called Ship Destination,

which involved simple calculations based on a diagram of ship locations (Table 6.1,

Carroll, 1993). The first-order factors that Carroll (1993) most frequently proposed

60

to load onto Gc were Verbal Ability, Language Development, and Reading

Comprehension, all factors which were dominated by verbal subject-matter content

(p. 599). A more recent example of the conflation of spatial and verbal factors with

fluid and crystallized factors is given by Benson, Hulac and Kranzler (2010).

Benson et al. (2010) found that the CHC model provided a better fit to the Fourth

Edition of the Weschler Adult Intelligence Scale (WAIS-IV) than the structure

proposed by the test designers. However, in the CHC model the Gc factor was

defined solely by loadings from tests of verbal subject-matter, and the Gf factor

received loadings from only visual-spatial tests plus the Arithmetic test.

In this study, we found again that the VPR model, which contains factors of

verbal content and perceptual-spatial content that transcend the fluid/crystallized

distinction, yielded better fit than models specifying a separate crystallized factors

(such as Gc and Gkn) and fluid factors (such as Gv and Gs). This finding thus

provides further evidence that the higher-order structure of intelligence is organized

along a Verbal-Perceptual-Image Rotation dimension rather than characterized by

broad factors distinguished mainly by their purported reliance on novel processing or

acquired knowledge.

3.4 Linking cognitive ability with personality

Establishing the VPR model as the best-fitting model among the three most

supported in the literature enabled exploration of the associations among cognitive

ability and personality and occupational interests, using the VPR model as the basis

for consideration of cognitive ability. The first step in this process was to examine

personality-intelligence associations.

The PT data provided an opportunity to look not just at linear associations but

also nonlinear associations, which require a large sample due to their relatively small

effect sizes. Some research in the gifted literature provided a basis for hypothesizing

that such nonlinear associations could be substantive at the right tail of the

intelligence distribution. Hypotheses for the linear associations were drawn

primarily from reviews of previous personality-intelligence research.

61

The analysis did not include specific abilities because there have been fewer

studies of specific abilities and personality, hence making hypotheses in this area

more difficult to formulate. A second reason was to keep the number of analyses

manageable. As in the first study, all eight PT grade and gender samples were used.

62

Chapter 4: Linear and nonlinear associations between general intelligence and personality

4.1 Introduction

Intelligence and personality are important predictors of behavior and

outcomes in many domains, notably in educational and occupational settings

(Barrick & Mount, 1991; Hunt, 2011). In addition, there are some associations

between intelligence and personality traits (Ackerman & Heggestad, 1997;

DeYoung, 2011). Within the Big Five framework, general intelligence (g) is most

strongly associated with Openness to Experience (r = .33 in the N-weighted meta-

analysis of Ackerman & Heggestad, 1997). This connection may seem obvious since

measures of Openness to Experience typically include items assessing engagement in

intellectual pursuits, and because intelligence has often been held to be the cognitive

part of personality (Cattell, 1950; DeYoung, 2011; Guilford, 1959). Nonetheless,

intelligence is also related to personality traits that are considered the least cognitive,

such as Extraversion and Neuroticism (DeYoung, 2011). Neuroticism has

consistently shown modest negative correlations with general intelligence (r = -.15 in

Ackerman & Heggestad, 1997), and most recent studies (performed after the year

2000) have shown that Extraversion also has a small but significant negative

association with g, in the range of r = -.04 to -.11 (Luciano, Leisser, Wright, &

Martin, 2004; Moutafi, Furnham, & Paltiel, 2005; Soubelet & Salthouse, 2011; Wolf

& Ackerman, 2005). In addition, DeYoung (2011) found that in 9 studies not

included in Ackerman and Heggestad’s (1997) meta-analysis, Conscientiousness had

a mean N-weighted correlation of -.12 with intelligence.

Nevertheless, some researchers have argued that the theoretical implications

of these personality-intelligence correlations are limited due to their small size or

inconsistency across studies (Eysenck, 1994; Soubelet & Salthouse, 2011; Zeidner,

1995). One possibility is that some intelligence-personality associations could be

nonlinear, and thus missed by traditional linear analyses (E. J. Austin, Deary, &

Gibson, 1997; E. J. Austin et al., 2002; Eysenck & White, 1964; Reeve et al., 2006).

Findings in this area have, however, have often been negative. Austin et al. (1997)

63

found evidence for positive quadratic effects of Neuroticism and Openness to

Experience on intelligence in one sample, but Austin et al. (2002) did not find any

significant effects of this kind for the Big Five and Eysenck’s Big Three in four other

datasets. There are three theoretical and methodological issues surrounding these

results.

First, different theories make alternative causal predictions about personality-

intelligence relations. For example, Ackerman’s PPIK theory (intelligence-as-

process, personality, interests, and intelligence-as-knowledge) predicts that

intelligence becomes related to personality through cognitive investment in four trait

complexes which involve different personality traits and interests (Ackerman, 1996;

Ackerman & Beier, 2003b). Alternatively, Chamorro-Premuzic and Furnham (2006)

proposed that personality-intelligence relations can be conceptualized as the

influence of personality traits on intellectual competence, where intellectual

competence is defined as “an individual’s capacity to acquire and consolidate

knowledge throughout the life span” (p. 259, Chamorro-Premuzic & Furnham,

2006). PPIK theory suggests that cognitive factors causally contribute to broader

constellations involving personality and interests (trait complexes), and thus that the

association between all the variables is an emergent property due to reciprocal

causation between all three variables. In contrast, Chamorro-Premuzic and

Furnham’s theory proposes that personality traits directly influence the development

of intelligence. A third possibility is that intelligence contributes directly to the

development of personality through conscious perceptions of adaptive benefit of

particular behaviours, or through the influence of intelligence on motivations.

When estimating only linear effects, it is difficult to distinguish these

possibilities without a longitudinal design, because effects are typically symmetrical

no matter which ways the causal arrows are drawn. However, nonlinear analyses can

pick up larger effects in one direction (e.g. there might be a quadratic effect of

intelligence on Extraversion but no quadratic effect of Extraversion on intelligence),

which can suggest that causal forces operated in this direction. Previous studies of

quadratic effects have focused on the quadratic effects of personality on intelligence

64

(E. J. Austin et al., 1997; E. J. Austin et al., 2002); however, in the current study we

assessed quadratic effects of intelligence on personality.

The second issue surrounding nonlinear personality-intelligence relations is

that previous nonlinear studies were not performed with latent variables as predictors

but with observed scores (E. J. Austin et al., 1997; E. J. Austin et al., 2002; Reeve et

al., 2006). This limited their power because the size of the quadratic effect was not

corrected for unreliability. Quadratic terms are particularly sensitive to unreliability

of the predictor variable (Moosbrugger, Schermelleh-Engel, Kelava, & Klein, 2009).

Methodological researchers have observed that even using factor scores for the

predictor can produce biased estimates of structural model parameters due to residual

measurement error (D. Bartholomew, 1987; Harring, Weiss, & Hsu, 2012).

Recently, Harring et al. (2012) found that, compared with methods that model latent

quadratic terms directly, the use of factor scores led to substantial underestimation of

quadratic coefficients.

A third issue is that to detect quadratic effects with small effect sizes, large

sample sizes are needed. Under simulation, Harring et al. (2012) showed that for a

medium-sized quadratic effect that accounted for 5% of the variance, even a small

sample size of 50 was sufficient to obtain power over .80. However, in practice,

quadratic or interaction effects can be considerably smaller than this, accounting for

only 1% or 2% of the variance. To find these effects, very large sample sizes (i.e. of

500 or greater) are necessary. For example, Moosbrugger et al. (2009) found that for

a quadratic effect size of 2% and a sample size of 400, average power was only 76%

using latent estimation (power would be less with non-latent methods). Thus, the

sample of Austin, Deary & Gibson (1997) and two of the four samples in Austin et

al. (2002) may not have had sufficient power to detect small quadratic effects.

Reeve, Meyer and Bonaccio (2006) conducted one study on personality-

intelligence relations that was sufficiently powered. Their study is directly relevant to

ours as we made use of the same sample so we review their analysis in detail. Reeve

et al. (2006) used a subsample of data from Project TALENT (PT), a nationally-

representative study of approximately 400,000 American high school students in

1960. The sample in Reeve et al. (2006) consisted of 71,887 students in their final

65

year of high school (seniors), with a mean of age 17.2 years (SD = 1.3). The ten PT

personality scales were developed specifically for the Project in the late 1950s,

before there was much consensus about models of personality structure. The scales

used thus do not correspond directly to the Big Five framework in common usage

today, but Reeve et al. (2006) related the scales to the Big Five by two methods.

First, the three authors independently examined each scale’s content and compared it

to the content of the NEO-PI-R scales (P.T. Costa & MacCrae, 1992), and second,

they re-administered the PT personality scales and IPIP scales for the Big Five to a

sample of 219 college students. Table 4.1 summarizes the NEO-PI-R facet with

which each PT scale was most closely associated (by rater consensus), as well as

with which Big Five trait(s) the scales loaded in a joint factor analysis with IPIP

scales (Reeve et al., 2006).

These relations provided a way to link the PT scales to the larger literature on

personality-intelligence relations, which has frequently been organized according to

the Big Five (e.g., Ackerman & Heggestad, 1997; Austin et al., 2002). The facet-

matching by Reeve and colleagues may be limited due to imperfect content overlap,

but the majority of the PT scales displayed good convergent validity with the Big

Five factors predicted to subsume them (factor loadings = .42 to .81). In addition,

the content of the PT scales was facet-like; hence they could be viewed as analogous

to facets of the Big Five, with the exception that some scales (e.g. Self-Confidence)

would be facets of more than one Big Five factor.

Table 4.1 Associations of the Project TALENT personality scales with the Big Five. PT scale NEO-PI-R facet Big Five trait loading(s)

a Sociability Gregariousness (E) E (0.69), A (0.38) Calmness Anger (ES) - reversed ES (0.69) Vigor Activity (E) E (0.43) Social sensitivity Sympathy (A) A (0.81) Tidiness Orderliness (C) C (0.79) Culture Aesthetics (O) O (0.51) A (0.44) Self-confidence Self-consciousness (ES) -

reversed E (0.60) ES (0.60)

Mature personality Achievement Striving (C) C (0.63) A (0.35) Impulsiveness Cautiousness (C) - reversed E (0.42) Leadership Assertiveness (E) E (0.51) O (0.41) a Loadings obtained in a joint factor analysis of the IPIP and PT scales by Reeve et al. (2006)

66

Reeve et al. (2006) found that g correlated positively and substantively

(above .15 in their definition) with the scales Mature Personality, Calmness, and

Self-Confidence in grade 12 males. These correlations were also observed in grade

12 females, where Culture and Social Sensitivity were also correlated positively with

g. These associations may, however, have been influenced by measurement artefacts

because the PT personality scales were nearly uniformly positively correlated with

each other. The mean of the inter-scale correlations in the senior sample was .38 in

males, and .35 in females (SD = .14 in both samples). Reeve et al. (2006) did not

address this common variance among personality scales (similar factors in other

personality inventories have been termed ‘general factors of personality’; Rushton &

Irwing (2008). This common variance was relevant because it correlated positively

with g in Project TALENT (mean r = .28 in all samples), and thus we predicted that

it would affect the correlations of the scales with g.

Recent research has suggested that the common variance between Big Five

measures is in large part due to rater bias. In a meta-analysis of 45 multi-trait multi-

method samples, Chang, Connelly and Geeza (2012) found that much of the common

variance between Big Five personality scales is due to method variance specific to

raters, which likely includes response biases such as socially desirable responding.

After rater effects were controlled for in the CTOM (correlated traits, orthogonal

methods) model, adding a general factor of personality (GFP) above the Big Five

factors resulted in a substantial decrement in model fit compared the model allowing

free covariance between the Big Five (Chang et al. 2012). Moreover, the GFP had

non-substantive loadings from Extraversion (.03) and Openness to Experience (-.09),

supporting the view that there is no single factor that sits above the Big Five in multi-

informant data (however, a model with Digman’s Alpha and Beta were still found to

be plausible by Chang et al., 2012). A number of studies have now supported the

conclusion that the GFP emerges for artifactual methodological reasons (Anusic,

Schimmack, Pinkus, & Lockwood, 2009; M.C. Ashton, Lee, Goldberg, & de Vries,

2009; Bäckström, Björklund, & Larsson, 2009; de Vries, 2011).

As detailed further below, we also observed that several of the PT scales in

Reeve et al. (2006), and in our initial analysis, displayed stronger positive

67

correlations with g than were expected based on such correlations in the Big Five,

with which the PT scales correlated. This, combined with the moderate correlation

of the GFP to g, suggested that common variance may have acted as a confounder in

the estimates. Because we were primarily interested in the relations of the individual

scales to g, and wished to err on the side of under-estimation rather than over-

estimation, we conducted our analyses while controlling for the GFP.

In addition to linear associations, Reeve et al. (2006) looked for nonlinear

relations by converting the personality scores into extremeness scores (X−Meanx|)

and examining their correlations with g factor scores. Reeve et al. (2006) did not

observe any correlations between the extremeness scores and g above a selected cut-

off of .15. However, there were two limitations to their method of looking for

quadratic effects. First, whereas extremeness scores may suggest the presence of

quadratic trends, they are not equivalent to examining true quadratic effects which

predict scores with the form |X2-Meanx|. Second, Reeve et al. (2006) chose to

convert the personality scale scores rather than the intelligence test scores in PT to

extremeness scores, thus examining the effect of extreme personality on intelligence

(Rosenthal & Rosnow, 1991). This is the same direction of effect investigated by

Austin and colleagues (Austin et al., 1997; Austin et al., 2002). As noted, we were

instead interested in examining the effects of intelligence on personality. This had the

added advantage of greater power, due to the greater reliability of the latent g factor

compared to the observed personality scales.

The aim of our study was to re-examine linear and nonlinear relations between g

and personality in Project TALENT, taking into account common variance among

the scales. Moreover, we used structural equation modeling (SEM), which avoids

using factor scores and allows for direct estimation of latent linear and quadratic

effects (Klein & Moosbrugger, 2000). In addition to SEM, we used generalized

additive models (GAMs; Hastie & Tibshirani, 1986) to explore further possible

nonlinear trends. The PT data were suited to our aims because of its large and

relatively population-representative sample of nearly 400,000 high school students in

four grades, allowing for the possibility of replication across grade subsamples.

68

4.1.2 Linear personality-intelligence associations in Project TALENT

We focused on the personality-intelligence literature (primarily on the Big Five)

in generating our hypotheses about specific associations.

Openness to Experience displays a positive correlation with g (Ackerman, 2009;

Ackerman & Heggestad, 1997; DeYoung, 2011), and the two PT scales that loaded

significantly on Openness to Experience were Culture and Leadership (Reeve et al.,

2006). Neither scale is a pure measure of Openness/Intellect (see Table 4.1);

therefore, we hypothesized that their correlations with g would be positive, but

smaller in size than the .33 value in meta-analysis of Ackerman & Heggestad, 1997.

Five of the ten scales in PT had primary loadings on Extraversion, which has

typically shown small negative associations with intelligence (Wolf & Ackerman,

2005; Moutafi, Furnham & Paltiel, 2005; Austin et al., 2002). However, this relation

is not uniform for all facets of Extraversion. Ackerman and Wolf (2005) suggested

that Extraversion should be split to reflect two different aspects: social closeness (the

need for intimacy) and social potency (the need for making an impact on others).

They also hypothesized that “Individuals high on social closeness may be less likely

to invest their time in intellectually engaging tasks, leading to lower scores on

intelligence tests” (p. 533, Wolf & Ackerman, 2005). Partially consistent with this,

their meta-analysis of 48 samples showed that the correlation between social potency

and intelligence was slightly positive (r = .04, p < .05), whereas the intelligence

association with social closeness was not significantly different from zero (r = -.01)

(Wolf & Ackerman, 2005). Similarly, Pincombe, Luciano, Martin & Wright (2007)

found that the excitement-seeking and gregariousness facets of NEO Extraversion

correlated negatively with IQ (r = -.09 and r = -.15, respectively). We thus

anticipated that PT Sociability and Impulsiveness scales would show negative

associations with intelligence (due to their face-value relations with social closeness

and excitement-seeking), whereas Vigor, Self-Confidence and Leadership would

show positive associations (due to their face-value relations with social potency).

The Big Five trait Neuroticism has a negative correlation with intelligence

(Ackerman & Heggestad, 1997; DeYoung, 2011). Based on their face-value

contents, and the findings of Reeve et al. (2006), the PT scales of Calmness and Self-

69

Confidence represent the converse of Neuroticism (Emotional Stability); therefore,

we predicted these scales would display positive associations with intelligence.

The literature has suggested that Big Five Conscientiousness has a small

negative association with intelligence (Moutafi, Furnham & Paltiel, 2005; DeYoung,

2011). In addition, in a sample of British adults, Moutafi, Furnham and Crump

(2003) found that the Orderliness facet of Conscientiousness in particular had a

negative correlation with g (r = -.18), which they argued may be because lower-

intelligence individuals use planning and organization to compensate for their

disadvantage on intellectual tasks (see also Chamorro-Premuzic and Furnham, 2006).

The PT scale Tidiness was related on a content basis to Orderliness by Reeve et al.

(2006); therefore, we hypothesized that it would have a negative correlation with

intelligence. The PT scale Mature Personality was also related to Conscientiousness

by Reeve et al. (2006), hence we predicted a negative association for it.

Big Five Agreeableness has typically not been found to have significant

correlations with intelligence (Ackerman & Heggestad, 1997; DeYoung, 2011);

hence we did not make any directional hypothesis regarding the PT scale Social

Sensitivity, which was the only PT scale with a high correlation with Agreeableness

according to Reeve et al. (2006).

4.1.3 Possible nonlinear associations

Although nonlinear associations between intelligence and personality have

rarely been found, some suggestive evidence for nonlinear associations has been

found in research on gifted children and adolescents. This has primarily been done

with the Myers-Briggs Type Indicator (MBTI; Myers, McCaulley, & Most, 1985).

A meta-analysis of 14 studies of gifted adolescents, mostly identified through

talent searches using the SAT and selection into gifted programs, showed that they

were substantially more likely to fall on one side of the dichotomous MBTI

dimensions than a norm group of students (Sak, 2004). Gifted adolescents were

more likely to select Introversion over Extroversion (48.7% compared to 35.2% in

the non-gifted sample), Intuition over Sensation (71.6% compared to 31.9%), and

Perceiving over Judging (60.1% compared to 45.4%), as well as marginally more

70

likely to prefer Thinking to Feeling (53.8% compared to 47.5%; Sak, 2004). Studies

in adults have found that MBTI Extroversion to be strongly related to Big Five

Extraversion (r = .74), whereas Intuition is strongly related to Openness to

Experience (r = .72); MBTI Thinking and Perceiving are moderately negatively

correlated with Agreeableness (r = -.44) and Conscientiousness (r = - .49),

respectively (correlations for the male sample in R. McCrae & Costa, 1989).

Therefore, by extension it can be predicted that gifted adolescents may be

substantially higher in Openness to Experience and lower on Extraversion,

Agreeableness and Conscientiousness than non-gifted adolescents. A recent study of

Israeli adolescents, who were selected as the top 1% to 3% of performers on an

intelligence test, confirmed this pattern for Openness to Experience (d = .51) and

Agreeableness (d = -.28), and also showed that gifted adolescents were lower in Big

Five Neuroticism than non-gifted adolescents (d = -.26) (Zeidner & Shani-Zinovich,

2011). Group differences in Conscientiousness and Extraversion were in the

expected direction based on MBTI studies, but non-significant (Zeidner & Shani-

Zinovich, 2011).

The presence of some substantial mean differences between gifted and

non-gifted groups suggests that average personality level might differ to an

expanding (e.g. exponential) degree with increasing ability level, although it is

possible that linear effects could produce these effects as well. Exponential functions

may most closely approximate differences in certain personality traits with

increasing ability level, but such trends would also be captured by quadratic effects,

at least for one side of the parabolic curve. One issue relating to this testing is that

some studies have also found increases in personality variance with higher

intelligence (e.g. in the MBTI; Myers & McCaulley, 1985). This may violate the

assumption of homogeneity of variance underlying generalized linear models,

although these models are robust to some level of heteroscedasticity (Tabachnick &

Fidell, 2007). It is possible that higher cognitive ability is causally linked to

increases in personality variance, as intelligence potentially facilitates more flexible

adjustment of personality to the environment; however, in this study we focused on

mean-level differences in personality.

71

Given the evidence in the gifted literature, we hypothesized that positive

quadratic trends would be observed for the PT scales associated with Openness to

Experience (Culture and Leadership) as well as Emotional Stability (Calmness and

Self-Confidence). We also predicted that negative quadratic effects (an inverted-U

shape) would be observed for the scales associated with the social closeness aspect of

Extraversion (Sociability), Agreeableness (Social Sensitivity), and Conscientiousness

(Tidiness and Mature Personality). Because less is known about personality in low-

ability groups, these predictions were based on the trend for above-average

intelligence.

4.2 Method

4.2.1 Sample

Project TALENT participants were obtained by a stratified random sample of all

public and private high schools in the United States in 1960 (Flanagan et al., 1962).

The PT dataset was thus a nationally-representative sample of approximately 5% of

the student population. The full sample consisted of 376,213 students, with

approximately 100,000 students in each grade from 9 through 12. Of the full sample,

50.13% was female. The age range was from a mean of 14.4 in grade 9 (SD = .78) to

17.3 in grade 12 (SD = .67). The full individual age range was 8 to 21.

4.2.2 Intelligence measures

The intelligence measures for the current study were selected from the PT

aptitude and achievement tests, using the broad selection of 37 tests as defined in a

previous study (for descriptions of the tests and reliabilities see Major, Johnson &

Deary, 2012; see also Flanagan et al., 1962).

The data screening methods for the intelligence tests were the same as used by

Major, Johnson and Deary (2012). Scores on the PT response credibility index,

which was based on a screening test assessing illiteracy, mental disability or an

apathetic testing attitude, were used to exclude participants who did not reach the

cut-offs set by the PT study designers, except where only mental slowness was

indicated (a low score for the number of responses on the Clerical Checking test).

Transformations were applied to three tests that displayed non-normal distributions

72

(Capitalization, English Usage and Table Reading), and cases showing severe

problems with multivariate outliers were removed (Major, Johnson & Deary, 2012).

Following data screening of the intelligence tests, total sample size was reduced to

366,939 (2.47% of the sample removed, the vast majority for low screening scores).

4.2.3 Personality measures

The PT personality scale scores were derived from 108 items that asked

students how typical certain personal attributes and behaviors were of them. Table

4.2 contains sample items for the scales; reliability coefficients from Reeve et al.

(2006) are presented due to their lack of availability from the original study. The

responses to personality items were on five-point Likert scale. The scores available

in the PT dataset were scale scores, which were obtained by assigning a score of 1 to

items where the student indicated that the item described them “extremely well” or

“quite well” (the two most affirmative responses), and a score of 0 to other responses

(“fairly well”, “slightly”, or “not very well”). The converse scoring method was

used for negatively-phrased items (Wise et al., 1979).

Table 4.2 Personality test descriptives. Scale Sample item Items Reliability

a

Sociability “I like to be with people most of the time” 12 .83 Calmness “I am usually self-controlled” 9 .81 Vigor “I am full of pep and energy” 7 .76 Social

sensitivity “I never hurt another’s feelings if I can avoid it” 9 .79

Tidiness “I like to do things systematically” 11 .85 Culture “I think culture is more important than wealth” 10 .69 Self-confidence “I am usually at ease” 12 .79 Mature

personality “I make good use of all my time” 24 .90

Impulsiveness “I usually act on the first plan that comes to

mind” 9 .69

Leadership “People naturally follow my lead” 5 .65 a Reliabilities from the sample of 219 college students in Reeve et al. (2006).

4.2.4 The general factor of personality

The raw PT personality scales displayed mean inter-correlations that ranged

from .35 in the grade 12 females (SD = .14), up to a maximum of .42 in the grade 9

males (SD = .13). Across the eight samples (four grades by two genders), the first

73

common factor accounted for a mean of 41.3% of variance (SD = 2.2%). Potential

sources for this common variance included artifactual sources such as method

variance (e.g. due to pencil-and-paper testing), acquiescence bias and socially-

desirable responding, and non-artifactual true score variance.

Although it was not possible to disentangle these sources directly, some

evidence suggested that this common variance was potentially confounding the

relations of personality scales with g. Several personality scales displayed

unexpectedly positive correlations with g. The Tidiness scale, which Reeve et al.

(2006) identified on a content basis with the Orderliness facet of Conscientiousness,

displayed a positive correlation with g in all samples (mean r = .16 in males, .10 in

females; see Appendix B). This observation contradicted the finding of Moutafi et

al. (2003) that Orderliness is negatively associated with g, and that

Conscientiousness in general in also negative related (DeYoung, 2011). Similar

inferences could be drawn for the Sociability and Impulsiveness scales, which were

predicted to have negative associations with g based on the literature, but instead

showed small positive correlations (Sociability: r = .09/.05 in males/females;

Impulsiveness: r = .03/.10 for males/females)8. We hypothesized that the positive

correlation between the GFP and g could account for these positive correlations

(mean r = .28 in both males and females).

In order to aid in the interpretation of the GFP, we performed a re-analysis of

the college sample data from Reeve et al. (2006)9. The general factor from the PT

scales, extracted through maximum likelihood estimation, explained 25.9% of the

variance in the college sample. The GFP was then correlated with the individual

items (item-level data were not available in PT). The Vigor scale was over-

represented in items that correlated most highly with the GFP: six of the seven items

assessing Vigor were in the top 10 most highly-correlated items, including the most

highly correlated item (“I am energetic”, r = .63). The Vigor scale also had the

highest loading on the GFP (.71) in the college sample. In addition to this trend, only

23 of the 108 personality items (21%) contained statements that referred to other

people’s views (e.g. “people consider me sociable”), but 8 of these items were in the

8 The correlation between Impulsiveness and g in grade 9 males was non-significant, however.

9 Data obtained through personal communication (September 18, 2012).

74

top 20 most correlated with the GFP (40%). This finding suggested that items that

primed reputational concerns were more closely tied to the GFP. In the college

sample, the GFP was most highly associated with items that seemed to tap the form

of socially-desirable responding that has been termed egoistic self-enhancement

(Paulhus & John, 1998). Nonetheless, this interpretation may not generalize entirely

to the PT sample, as indicated by differences in the GFP loadings in the two samples.

In the college sample a lower loading was seen particularly on Tidiness (.29,

compared to .70 in PT). This finding indicated that Tidiness was more integral to the

GFP in PT, suggesting that the GFP in PT had more to do with Conscientiousness

than in the college sample.

Regardless of whether the correlation between the personality factor and g

was artifactual or not, our primary interest was in the relations of the individual

scales with g. Therefore, we decided to remove influence of the common variance

from the scales. The scales were regressed onto the GFP, and residuals retained for

the further analyses. To verify that the residualization did not damage the

convergent validity of the PT scales we examined their correlations with the

predicted IPIP Big Five scales in the college sample. Compared to the mean

correlation of the unresidualized scales (r = .56, SD = .13), the mean correlation

decreased to r = .36 (SD = .19). This reduction was consistent with the high

correlation of the GFP in the PT scales with the GFP in the IPIP scales (r = .77).

When the variance in the PT GFP that was explained by the IPIP GFP was removed

(through regression) prior to using it to residualize the PT scales, there was no

reduction in the correlation between the residualized PT scales and the IPIP scales

(mean r = .56, SD = .12). Removing the PT GFP appeared to reduce the convergent

validity of the PT scales, but this seemed to be because the IPIP and PT scales shared

method or rater variance, captured by their GFPs, which inflated the initial

correlations. The results of the analyses with the original scales are presented in the

supplemental materials (Appendix B), but the focus of all further presentation is on

the residualized scales.

Following residualization for the GFP, the personality scales were screened

for normality and outliers. Outliers were capped at four standard deviations above

75

and below the mean (approximately the most extreme score expected in our

samples). The scales Impulsiveness and Leadership displayed positive skewness in

all samples, therefore a square-root transformation (with reflection) was applied to

them. Following these transformations, all personality scales displayed adequate

normality (all skewness and kurtosis z values below 0.5). In contrast to the raw

scales, the mean correlation between the residualized scales was slightly negative,

and ranged from -.096 (SD = .12) in grade 11 females to -.099 (SD = .09) in grade 9

males. Table 4.3 displays the correlations in grade 10 males and females.

76

Table 4.3 Correlations among personality scales after removal of the general personality factor (frade 10 males/females).

Sociability

Social

Sensitivity Vigor Calmness Tidiness Culture Self-

confidence Mature Personality

Impul-

siveness Leadership Sociability – .019 .182 -.160 -.160 -.183 .109 -.360 .059 -.035 Calmness -.037 – -.169 -.106 -.189 -.120 -.201 -.278 -.007 -.150 Vigor .114 -.187 – -.186 -.189 -.183 -.002 -.150 .104 .020 Social sensitivity -.133 -.106 -.165 – -.112 -.213 .056 -.164 -.130 -.164 Tidiness -.162 -.203 -.157 -.155 – -.052 -.191 -.004 -.179 -.200 Culture -.127 -.068 -.191 -.202 -.041 – -.182 -.211 -.047 -.133 Self-confidence .049 -.194 -.069 .082 -.116 -.143 – -.081 -.012 .030

Mature personality -.339 -.261 -.130 -.168 -.080 -.231 -.032 – -.085 -.040

Impulsiveness .065 -.037 .035 -.095 -.145 -.048 -.072 -.103 – .116

Leadership -.051 -.082 -.043 -.145 -.186 -.092 -.049 -.079 .118 –

Male correlations are below the diagonal, females above.

77

4.2.5 Methods of analysis

We searched for linear and nonlinear associations between g and the personality

scales in two ways. The first method was to estimate linear and quadratic effects

using latent moderated structural equation modeling (LMS; Klein & Moosbrugger,

2000). LMS directly models the quadratic term as the interaction of a latent variable

with itself (or the square of the variable), and corrects for the multivariate non-

normality of the term, making it a better method than regression (Harring, Weiss &

Hsu, 2012; Moosbrugger, Schermelleh-Engel, Kelava & Klein, 2009). LMS was

performed in Mplus 5.21.

Secondly, we ran generalized additive models (GAMs; Hastie & Tibshirani,

1986), using the R package ‘mgcv’ (Wood, 2006). A GAM is a generalized linear

model in which the linear predictor depends on unknown smooth functions of the

predictor variables. The smooth functions are represented by regression splines with

a particular basis function (for our analyses, the cubic basis was selected). The

degree of smoothing of the spline is determined by the generalized cross validation

score, which is a measure of how well the spline fits across datasets with each datum

left out in turn (see Wood, 2006, for more details). We used GAMs to explore other

possible nonlinear trends apart from quadratic trends between the personality scales

and g.

Using the LMS and GAM approaches, we estimated the effects of g on the

ten personality scales in each of eight samples divided by grade and sex. We

selected the direction of effect of g on personality because we preferred this direction

theoretically and because g was more reliably measured than the personality

variables. In addition, it was not possible to estimate latent personality traits because

of the lack of item-level data. Thus, g as a predictor allowed for LMS estimation of

quadratic effects.

The measurement model used for g was the VPR model, which has been

shown to fit well to these data (Major, Johnson & Deary, 2012). The variance

explained by each effect in the LMS models was obtained by subtracting the residual

variance of the personality scales from 1 (as the personality scales were

78

standardized). GAMs were estimated with g factor scores obtained from the VPR

model.

Missing data were handled with through direct maximum likelihood

estimation, which requires the assumption the data were missing at random (MAR).

This assumption was tenable in PT because it is unlikely that students purposely

avoided particular aptitude tests or the personality scales. In addition, only 2.3 to

3.2% of the ability test scores and 1.0 to 3.5% of the personality test scores were

missing in each sample.

4.3 Results

Tables 4.4 and 4.5 display the standardized linear and quadratic effects of g

on the residualized personality scales for males and females, respectively. Figure 4.1

(males) and Figure 4.2 (females) illustrate the predicted mean-level differences in

personality based upon the estimated linear and quadratic effects in the grade 10

samples.10

Social Sensitivity in males and Calmness in females were omitted from

the figures due to the lack of significant linear or quadratic effects.

In the male samples (Table 4.4, Figure 4.1), the largest linear effects of g

were on Sociability (beta = -.042 to -.130), Calmness (beta = .076 to .104), and Self-

Confidence (beta = .106 to .131). Substantial negative quadratic effects (R2

of

approximately 2% or greater) were observed for Sociability (beta = -.146 to -.159)

and Vigor (beta = -.107 to -.116). Positive quadratic effects were observed for

Mature Personality (beta = .099 to .119) and Leadership (beta = .106 to .124).

In the female samples (Table 4.5, Figure 4.2), the largest linear effects of g

were on Sociability (beta = -.077 to -.195), Tidiness (beta = -.064 to -.163), and

Mature Personality (beta = .075 to .140). Substantial negative quadratic effects were

seen on Sociability (beta = -.140 to -.155) and Tidiness (beta = -.064 to -.163), and a

positive quadratic effect was found on Mature Personality (beta = .075 - .140).

10

The figures are at the end of the Results section. A separate page for figure titles and captions is

provided due to lack of space.

79

Table 4.4

Standardized linear and quadratic effects of g on the personality scales (males).

Trait Linear effect Quadratic effect

Gr. 9 Gr. 10 Gr. 11 Gr.12 Gr. 9 Gr. 10 Gr. 11 Gr. 12

Sociability

Beta -.042 -.087 -.118 -.130 -.146 -.148 -.155 -.159

R2 .002 .007 .014 .017 .032 .034 .037 .039

Calmness

Beta .076 .094 .101 .104 – – – –

R2 .006 .009 .010 .011 – – – –

Vigor

Beta .053 .020 – -.022 -.114 -.116 -.107 -.110

R2 .003 .000 – .000 .019 .020 .017 .019

Social Sensitivity

Beta -.029 – .017 .020 – – – -.016

R2 .001 – .000 .000 – – – .000

Tidiness

Beta – -.020 -.058 -.086 -.072 -.069 -.084 -.088

R2 – .000 .003 .007 .008 .008 .011 .013

Culture

Beta -.087 -.073 -.082 -.067 .024 .054 .077 .093

R2 .008 .005 .007 .005 .000 .004 .008 .011

Self-Confidence

Beta .131 .107 .106 .118 – – – .026

R2 .017 .011 .011 .014 – – – .001

Mature Personality

Beta .066 .061 .070 .056 .119 .115 .113 .099

R2 .004 .004 .005 .003 .022 .021 .020 .016

Impulsiveness

Beta -.125 -.076 -.032 -.023 .031 .021 .028 .018

R2 .016 .006 .001 .000 .001 .000 .001 .000

Leadership

Beta -.116 -.094 -.048 – .124 .113 .107 .106

R2 .013 .009 .002 – .023 .022 .017 .016

Effects greater than .015 were significant at p < .001, with no adjustment for multiple testing. Non-significant

effects are not shown.

80

Of our nine hypotheses about the linear effects of g on the personality traits,

five were supported in all male samples (positive: Calmness, Self-Confidence;

negative: Sociability, Tidiness, Impulsiveness), and one more received support in

some grades (the positive effect on Vigor)11

. In females, four hypotheses were

supported (positive: Culture, Self-Confidence; negative: Sociability, Tidiness) and

two had mixed support (the positive effect on Vigor and negative effect on

Impulsiveness). The most unexpected finding was a negative linear effect of g on

Leadership. This effect, in combination with the positive quadratic effect of g on

11

The negative linear effect of g on Tidiness in grade 9 males was only significant at p < .05, and

hence would not survive correction for multiple testing. Due to the effects in the other three samples,

however, we counted this effect as significant across all samples.

Table 4.5

Standardized linear and quadratic effects of g on the personality scales (females). Trait Linear effect Quadratic effect

Gr. 9 Gr. 10 Gr. 11 Gr.12 Gr. 9 Gr. 10 Gr. 11 Gr. 12

Sociability

Beta -.077 -.115 -.161 -.195 -.153 -.155 -.149 -.140

R2 .006 .013 .026 .038 .036 .037 .036 .031

Calmness

Beta – – – – – – – –

R2 – – – – – – – –

Vigor

Beta .041 – – -.032 -.074 -.077 -.065 -.050

R2 .002 – – .001 .008 .009 .007 .004

Social Sensitivity

Beta – – .017 – -.052 -.047 -.079 -.075

R2 – – .000 – .004 .008 .010 .009

Tidiness

Beta -.064 -.105 -.137 -.163 -.092 -.100 -.106 -.120

R2 .003 .011 .019 .027 .015 .016 .018 .023

Culture

Beta .032 .066 .071 .078 .030 .042 .045 .080

R2 .001 .004 .005 .006 .001 .003 .003 .005

Self-Confidence

Beta .047 .040 .034 .055 – .022 .039 .043

R2 .002 .002 .001 .003 – .000 .003 .003

Mature Personality

Beta .075 .083 .133 .140 .162 .166 .149 .122

R2 .006 .007 .018 .020 .041 .042 .035 .024

Impulsiveness

Beta -.036 .046 .055 .075 .069 .079 .046 .046

R2 .001 .002 .003 .006 .008 .010 .003 .003

Leadership

Beta -.111 -.109 -.079 -.031 .095 .093 .096 .106

R2 .012 .012 .006 .001 .014 .013 .014 .017

Effects greater than .015 were significant at p < .001, with no adjustment for multiple testing. Non-significant

effects are not shown.

81

Leadership, resulted in the highest levels of Leadership being observed for those with

low g (see Figures 4.1 and 4.2). This finding may be less trustworthy than the others,

however, because the Leadership scale only contained five items, and displayed

borderline reliability (alpha = .65) in Reeve et al. (2006). We reserve interpretation

of the meaning of the effects for the Discussion.

Of our eight predicted quadratic effects, four were supported in male samples

(positive: Culture, Leadership; negative: Sociability, Tidiness), and six were

supported in female samples (positive: Culture, Self-Confidence, Leadership;

negative: Sociability, Social Sensitivity, Tidiness). The most important deviation

from our hypotheses was for the Mature Personality scale, which was predicted to

have a negative quadratic association with g, but instead had a positive one.

4.3.1 LMS results compared to GAM results

Figure 4.3 shows a comparison of the fitted functions in the LMS and GAM

models for the example of Sociability in grade 10 males. As can be seen, the

predicted personality levels are similar in both models. In general, visual inspection

of GAM-predicted values showed a close correspondence with LMS results,

indicating that a combination of linear and quadratic effects gave a good

approximation of the relations revealed by the GAMs (other graphs of the GAMs are

available from the first author). In addition, the R2 for the GAMs were consistent

with the variance explained by the combination of the linear and quadratic effects of

LMS (slightly more variance was accounted for in the LMS models due to the use of

a latent g factor instead of factor scores). For the GAMs in males, the three

personality traits where g predicted the most variance were Sociability (3.3%),

Leadership (2.1%) and Tidiness (1.7%). In females it was Sociability (4.4%),

Mature Personality (3.7%), and Tidiness (2.5%). The variance explained in the

personality scales was higher for females than males in a number of cases, although

this varied greatly across scales (see Tables 4.4 and 4.5). Table B3 in Appendix B

compares the AICs for the GAM models to the null models in the grade 10 samples.

Compared with the null models, the GAM models all displayed lower AICs,

indicating that they added predictive power compared to a model with no predictors

(and were better fitting).

82

4.3.2 Grade and sex differences

The comparison of grade and sex differences in the estimated personality-

intelligence relations requires the assumption of measurement invariance between the

samples for intelligence and personality. This assumption could not be tested for the

personality scales due to lack of item-level data. In addition, measurement

invariance testing revealed that although configural and weak invariance were

tenable across grade and sex the VPR intelligence model, strong invariance (equality

of the intercepts) was not supported in both cases, as indicated by an decrease in CFI

> .010 (Cheung & Rensvold, 2002). Therefore, the differences in personality-

intelligence relations across samples must be interpreted with caution, as they may be

attributable to differences in the measurement of intelligence (or personality).

With this caveat in mind, there were some differences in the measured

relations across grade. Notably, the linear relation of Sociability with g was more

negative with increasing grade level (comparing grade 9 to grade 12 in males: ∆ beta

= -.88, log-likelihood ratio test: 2 (1) = 263.58, p < .001; in females: ∆ beta = -.118,

2 (1) = 514.98, p < .001). Two other important trends were the reduction of a

negative association of Leadership with g at higher grades (in males, ∆ beta =.106,

2 (1) = 387.06, p < .001; in females, ∆ beta = .080 2 (1) = 233.40, p < .001) and an

increase of the negative association of g with Tidiness (in males, ∆ beta = -.075, 2

(1) = 189.34, p < .001; in females, ∆ beta = -.099 2 (1) = 364.73, p < .001).

4.3.3 Figures 4.1 to 4.3: titles and captions

Figure 4.1 Mean personality as predicted by general intelligence (grade 10 males).

Caption: Personality scales and g are in standard units. Light lines represent 2

standard errors (SEs) above and below the mean (approximate 95% confidence

interval). SEs obtained from GAM models.

Figure 4.2 Mean personality as predicted by general intelligence (grade 10 females).

Caption: Personality scales and g are in standard units. Light lines represent 2

standard errors (SEs) above and below the mean (approximate 95% confidence

interval). SEs obtained from GAM models.

83

Figure 4.3 LMS and GAM-predicted sociability as a function of general intelligence

(grade 10 males).

Caption: LMS estimate = solid grey line. GAM estimate = dashed line. Sociability

and g are in standard units.

84

Figure 4.1

85

Figure 4.2

86

Figure 4.3

87

4.4 Discussion

In this study we examined linear and quadratic associations between g and

personality in Project TALENT. SEM was used to estimate linear and quadratic

effects of latent g on ten personality scales, and the influence of the general factor of

personality was controlled by residualizing the personality scores for the GFP. A

review of literature provided us with seventeen hypotheses of linear and quadratic

associations; nine of these hypotheses (53%) received support in all male samples

and ten (58%) received support in all female samples. In this section, we first review

the observed associations and discuss in greater detail some of the unexpected and

theoretically-relevant results. We then outline limitations of the study, and the

implications of our results for future research.

In divergence from previous studies (E. J. Austin et al., 1997; E. J. Austin et

al., 2002; Reeve et al., 2006) that have not done so, we found significant quadratic

associations of g with aspects of personality. Sociability, Vigor, Mature Personality

and Leadership were associated in this manner in males, and Sociability, Tidiness

and Mature Personality in females. These associations accounted for at most 3.9% of

the variance, so it would not be appropriate to conclude that prior studies have misled

the field in finding only small quadratic associations. Still, the associations we found

would have importance in considering mean personality scores of groups differing

greatly from average g. In our strongest example, using the grade 10 female sample,

negative linear and quadratic associations with g predicted a mean Sociability level

.70 SDs lower (SE = .02) for individuals two SDs above the mean on g, compared to

individuals of average g12

. Such a difference would generally be considered

substantive, though it did not render the mean Sociability level of 10th

grade females

with high g particularly unusual (the mean fell at approximately the 27th

percentile of

Sociability of the full sample). The group difference due to the linear effect alone

would be only .23 SD (SE = .02), accounting for 1.3% of the variance, compared to

3.7% of the variance in the model with the quadratic effect. This illustrates that

failing to consider nonlinear relations causes underestimation of the true associations

between certain personality traits and intelligence. Recognition of these nonlinear

12

For individuals with low g (two SDs below the mean), Sociability was .24 SDs below the mean.

88

associations can be particularly important when focus is on personality in groups

with extremely low or high levels of g and/or in understanding how the development

of personality and intelligence is intertwined in individuals.

Our results were generally consistent with previous findings on intelligence-

personality associations in samples of the general population and gifted adolescents

(Ackerman & Heggestad, 1997; Zeidner and Shani-Zinovich, 2011). We found that

males and females with higher g tended to have higher Self-Confidence, and males

with higher g also averaged higher Calmness; these scales reflected lower

Neuroticism in Five-Factor terms (Reeve et al., 2006). For the scales likely

reflecting Extraversion, Project TALENT participants with higher g scores tended to

display lower Sociability but higher Leadership, which was in line with the

hypothesis of Ackerman & Wolf (2005) that intelligence is linked to lower social

closeness but higher social potency. We found some indirect support for lower

Conscientiousness among more intelligent adolescents (for Tidiness, but not Mature

Personality), as observed by DeYoung (2011), and lower Agreeableness (Social

Sensitivity, but in girls only), as found in gifted studies (Sak, 2004; Zeidner and

Shani-Zinovich, 2011). Openness to Experience was incompletely represented by the

Culture and Leadership scales, but participants with higher g scores were above-

average on these scales, with the exception of Culture in grade 9 males. This

replicated the most common association in personality-intelligence studies

(Ackerman & Heggestad, 1997; DeYoung, 2011).

General intelligence was associated with mean-level differences in all Big

Five domains, which is somewhat at odds with existing theories of personality-

intelligence relations. For example, Chamorro-Premuzic and Furnham (2006)

maintain that each of the Big Five should be related to intellectual competence, but

regard Agreeableness as a marginal indicator, and view Neuroticism as mainly being

related to intelligence through test anxiety, and Extraversion related through test-

taking style. The opposing associations with g that we observed for Sociability and

Leadership (two aspects of Extraversion), cannot be well-explained within their

framework. PPIK theory also does not provide a full explanation for broad

associations between g and the Big Five. In PPIK theory, g is mainly associated with

89

personality due to the involvement of group abilities (such as crystallized intelligence

and perceptual speed) in particular trait complexes. Most notably, crystallized

intelligence is thought to contribute to the Intellectual/Cultural trait complex, along

with Openness to Experience (Ackerman & Heggestad, 1997; Ackerman & Beier,

2006). However, many of the associations we observed in the current study would

not be predicted in this framework, such as the association between higher g and

lower scores on scales reflecting Conscientiousness and Agreeableness, as well as

the differential associations of g with social closeness and social potency (Ackerman,

2005). For instance, Ackerman & Heggestad (1996) stated that: “Intelligence-as-

process correlates weakly with most broad personality factors, except [negatively]

for those that are associated with psychopathology” (p. 239).

Our results suggest instead that there are meaningful associations between g

and each of the Big Five (and/or their facets). Moreover, g is closely related to

intelligence-as-process or fluid intelligence (Gustafsson, 2002; Kvist & Gustafsson,

2008; Major, Johnson, & Deary, 2012). Overall, our results were more consistent

with the personality differences observed in studies of gifted adolescents, such as a

greater tendency towards Perceiving (which is correlated with lower

Conscientiousness; McCrae & Costa, 1989) and Introversion on the Myers-Briggs

Type Indicator (Sak, 2004) and lower Agreeableness in the Big Five (Zeidner and

Shani-Zinovich, 2011). Because personality differences have been more apparent in

these studies, it seems likely that considering the developmental differences between

gifted and normally-developing children and adolescents may be a good way to

develop theories of personality-intelligence associations, in addition to examining

associations in the general population. It is possible, however, that gifted people

may have distinct life experiences (such as experience with accelerated education

programs) that make comparisons with general samples more difficult.

Due to quadratic associations of g with personality, adolescents with low g

did not necessarily display the converses of the personality associations of those with

high g, and in fact were more similar in score with high-g students than average

ability students on a number of scales. For example, like high-g students, they

averaged lower Sociability and Tidiness. Participants with low g were also

90

unexpectedly found to average higher scores on the Mature Personality and

Leadership scales than average-ability students.

Due to the positive association between Mature Personality and

Conscientiousness in Reeve et al. (2006), and the negative association of g with

Conscientiousness in the literature, we predicted a negative quadratic effect of g on

Maturity. The unexpected positive quadratic effect may reflect the fact that the

Mature Personality scale contained several items that tapped self-assessed

achievement striving and engagement (“I work fast and get a lot done”; “I am

productive”). Thus, it may not be so surprising that students with higher g scores

(who also tended to have had more success at school) also obtained higher scores on

this scale, possibly despite its association with Conscientiousness, on which they

tended to score lower.

The most unexpected linear association was the negative association between

g and the Leadership scale, mostly found in the lower grade levels (in grade 9

males/females, beta = -.12/-.11). This finding may have reflected lack of clear

understanding of the items by younger and less able students (e.g. the item “I am

influential”). Another possibility is that the students understood the items, but that

less intelligent students overestimated their leadership abilities due to lower

metacognitive ability to assess their social function (Kruger & Dunning, 1999). This

‘Dunning-Kruger’ effect may also apply to our finding of higher scores on the

Mature Personality scale for individuals with below-average g. One final possible

interpretation for the Leadership scale finding is that leadership in younger grades is

often more social than intellectual in nature, and that social engagement may be

negatively related to intellectual performance due to an investment trade-off between

social and intellectual activities (Ackerman & Wolf, 2005).

Most of the linear and quadratic associations we observed were present in

both sexes. The exceptions to this were linear associations of g with Culture,

Impulsiveness and Calmness, and a quadratic effect on Social Sensitivity that was

only present in females. Based on the content of the Culture scale and its positive

association with Big Five Openness (Reeve et al. (2006), we predicted that Culture

would show a positive association with g. This was the case in females, but small

91

negative associations were found in males (beta = - .067 to -.087). This may reflect

the fact that the Culture scale emphasized having good manners over intellectual

interests. Perhaps the socialization pressures on girls to be well-mannered were

stronger than those on boys. The other sex differences we observed were less readily

interpretable.

For scales where an association was found at any grade level, the majority

were found in all four grade samples: 13 of 19 in males (68.4%) and 14 of 18 in

females (77.8%). This observation supports the view that the effects were not due to

chance measurement artifacts from individual samples.

As noted in the Results, even if most effects were consistent, some effects

varied substantively in magnitude across grades. These differences have also been

the subject of prior theories on personality-intelligence relations (Ackerman & Wolf,

2005; Chamorro-Premuzic & Furnham, 2006). The increase of the negative

quadratic effect of g on Sociability across grades provides support for the hypothesis

that higher social closeness may run counter to the development of intelligence

because adolescents with greater need for social closeness select social activities

more frequently than (generally) solitary intellectual activities (Ackerman & Wolf,

2005). However, the direction of effect assumed and measured in this study may

imply that it was instead higher intelligence that reduced social closeness over time

due as higher-g students increasingly selecting more solitary activities. Due to the

unavailability of item-level data, the PT personality scales were not well-suited to

testing the quadratic effect of personality on intelligence (the effect hypothesized by

Ackerman & Wolf, 2005). Future studies may be able to disentangle these two

effects by comparing the sizes of quadratic effects in each direction.

The negative linear association of g with Tidiness became stronger with grade

level, which is consistent with the hypothesis of Chamorro-Premuzic and Furnham

(2006) that lower intelligence leads to the development of greater orderliness over

time as a compensatory mechanism to meet environmental demands13

. However, the

13

We tested this hypothesis alternatively by examining differences in mean Tidiness with the sample

split by quintile of g. Comparing grade 9 to grade 12 samples, Tidiness increased significantly in

every quintile (p <.001), but there was a progressively greater increase in Tidiness corresponding to

lower quintile of g.

92

negative quadratic effect of g on Tidiness also indicated that very low levels of g

corresponded with decreased Tidiness, possibly because very low intelligence is a

hindrance to orderly behavior.

There are several limitations surrounding our conclusions regarding

personality-intelligence relations. First, our personality scales may not have been

measurement invariant across different levels of g, which could have caused apparent

linear and nonlinear associations that did not exist (McLarnon & Carswell, 2012;

Waiyavutti, Johnson, & Deary, 2012). We had, however, no way to test this as we

did not have access to the items. Second, we were able to establish that measurement

invariance did not hold across samples for g. Thus, although we observed some

consistency of associations across grades and sexes, the constructs measured across

the samples may not have been identical.

The PT personality data had a large GFP that accounted for approximately

40% of the variance in each sample. Our removal of the GFP may have been a

limitation because it may have contained substantive personality variance, although

most recent research supports a largely artifactual origin of the GFP (Anusic et al.,

2009; M.C. Ashton et al., 2009; Bäckström et al., 2009; Chang et al., 2012). One

possible explanation for the large GFP in Project TALENT is that the context of in-

school testing may have influenced students to “fake good” on the personality scales,

and the more intelligent students were more capable and/or more motivated to do so.

Given that the main purpose of PT (of which the students were aware) was to assess

scholastic talent, it would be most relevant for students to exaggerate scores on

scales tapping behaviour socially desirable in the school context (such as diligence

and responsibility). The high loading of the Mature Personality scale on the GFP

(mean r = .79) was consistent with this interpretation, as was the relatively higher

loading of Tidiness on the GFP in PT samples compared with the college sample of

Reeve et al. (2006). A possible non-artifactual explanation is that more intelligent

students were in fact more successfully socialized within the high school

environment, and that this led to higher scores on all the PT personality scales.

Discounting this, however, studies of students selected for high intelligence found

that they did not score higher than unselected students on Agreeableness and

93

Conscientiousness—Big Five factors that reflect greater socialization (Sak, 2004;

Zeidner & Shani-Zinovich, 2011). The alternative of including the GFP could have

led to exaggerated g-personality associations.

Controlling the GFP in the current study caused a number of the personality

scales to have negative linear associations with g, in contrast with the results of

Reeve et al. (2006), who found only positive linear associations. Nonetheless, out of

eight positive-direction associations in Reeve et al. (2006), six were also found here.

The exceptions were the positive associations of g with Social Sensitivity and

Calmness in females. Replicated associations were with Mature Personality,

Calmness and Self-Confidence in males; Mature Personality, Culture, and Self-

Confidence in females. These results confirm that the GFP was not entirely

responsible for the positive associations observed in the previous study.

Nonetheless, one key implication of our results is that the GFP can be a

potentially important confounder or mediator of personality-g associations,

particularly for linear associations as these relations were the most affected by

removal of the GFP (see Supplemental Tables B1 and B2). If the GFP represented at

least partly substantive variance instead of methodological variance, it could have

been a mediator, whereby the effect of g on personality occurred indirectly through

the GFP, or vice-versa.

One final limitation to our study was that the sample was assessed in 1960,

and relations between personality and intelligence may have shifted since then. This

kind of change was observed by Wolf and Ackerman (2005), who that the relation

between Extraversion and intelligence was slightly positive before 2000, but slightly

negative after 2000. One notable source of such change concerns the erosion of

gendered occupational roles since then. Girls at that time had less opportunity to

aspire to high education, and especially to occupational achievement in their own

names. Moreover, it was very common that they aspired and expected to marry and

be supported financially by their husbands. Despite this possibility, females had

higher scores than males on the Mature Personality scale at all grade levels, and the

association between g and Maturity was higher in females than males (see Tables 4.4

and 4.5). Offsetting the age of the sample, one of the strengths of the Project

94

TALENT sample was that it was representative of the United States in 1960

(Flanagan et al., 1962), so that our results can be generalized to the whole population

at that time.

4.4.1 Conclusions and future directions

We found that mean levels for most Project TALENT personality scale scores

varied substantially across levels of g, and a number of scales showed quadratic

associations. These results provide further support for the view that personality-

intelligence associations are substantive and relevant to understanding the

development of individual differences in both domains (Ackerman, 1996; DeYoung,

2011; Sak, 2004). Our results also indicated two directions for future research in this

area: the interpretation of the general factor of personality, and the use of nonlinear

models to test the direction of effect (personality on intelligence, or intelligence on

personality).

If it was not controlled, the GFP would have had a substantial effect on

personality-intelligence relations in the current study due to its positive association

with g. Future research should examine whether this relation is substantive or

artifactual in nature, possibly through the use of multiple raters or social-desirability

scales. If the GFP itself is found to be largely artifactual, as much recent research

suggests, then it is questionable whether the g-GFP association can represent

meaningful variance, but research in this area is still ongoing.

The potential to examine direction of effect deserves more consideration in

personality-intelligence research. In the current study, we focused on the

associations of the quadratic function of g with personality, but such nonlinear

associations may be found in the other direction, or in both directions. Although the

nonlinear associations we observed were small in terms of variance explained, they

were capable of resulting in substantive personality differences for individuals at the

extreme ends of the g distribution. Nonlinear associations that result in substantive

differences in personality at the tails of the intelligence distribution, or differences in

intelligence at the tails of personality distribution, can potentially be very informative

about how personality and intelligence interact with each other. In spite of this, it is

likely that the direction of influence runs both ways in most cases, and that the

95

strength and direction varies depending on the environment and over time. In order

to understand the interplay between personality and intelligence more complex study

designs and models are needed.

4.5 Integrating cognitive abilities and interests

After examining personality-intelligence associations in PT, the next step was

to bring occupational interests into the research. As presented in the introduction, I

decided to focus on the possibility of replicating the trait complexes composed of

cognitive abilities and interests in PPIK theory (Ackerman & Heggestad, 1997). The

proposed capacity of these trait complexes to predict future occupation is a key

aspect of the theory, and a strength of the PT dataset was its longitudinal data. Thus

the trait complexes were examined in the context of occupation eleven years after

high school, where the hypothesis was that they should have equal predictive validity

to the use of individual scores for cognitive abilities and interests. This was tested

for trait complexes composed both of factors and latent classes. In addition, the trait

complexes were evaluated in terms of how well they matched their descriptions in

PPIK theory.

96

Chapter 5: Trait complexes of cognitive abilities and interests and their predictive validity for occupation

5.1 Introduction

Intelligence and interests are both important predictors of occupational

attainment and job type (De Fruyt & Mervielde, 1999; Kuncel, Hezlett, & Ones,

2004; Schmidt & Hunter, 2004). General intelligence (g) relates strongly to

occupational level (for review, see Schmidt & Hunter, 2004) Specific abilities such

as spatial and verbal abilities are also relevant to employment in specific

occupational areas such as the humanities and scientific fields (Johnson & Bouchard,

2009; Wai, Lubinski, & Benbow, 2009). In keeping with their applied purpose,

measures of occupational interests are predictive of the nature of future employment

(J. T. Austin & Hanisch, 1990; De Fruyt & Mervielde, 1999), as well as of

performance in that employment (Van Iddekinge, Putka, & Campbell, 2011).

Given the predictive validity of interests and cognitive abilities, discovering

any overlap between them could have theoretical as well as applied significance

(Johnson & Bouchard, 2009). Greater understanding of the links between cognitive

abilities and interests could aid our theories of both cognitive and interest

development. Some researchers have proposed that cognitive abilities have

substantial roles in the development of interests. Gottfredson’s (1986, 2005) theory

of circumscription and compromise posits that individuals’ self-awareness of their

levels of general intelligence influences their interest in particular occupations

according to their cognitive complexity. Hogan and Roberts’ (2000) socioanalytic

model of identity development proposed that interests are built on successful

experiences with cognitive investment, which depend on intelligence. In turn,

interests are theorized to influence the development of intelligence through the

selection of future learning environments (Hogan & Roberts, 2000; Scarr, 1996).

Development of a framework that better integrates interests and cognitive abilities

could lead to better career counseling advice, and long-term increases in person-

environment fit, which refers to matches between work environments and

individuals’ abilities and preferences (Dawis & Lofquist, 1984; Holland, 1997).

97

Increases in person-environment fit could have benefits for both individuals’ work

satisfaction and productivity.

In spite of the potential importance of integrative research, there have been

relatively few studies of the associations between interests and cognitive abilities

(Ackerman & Heggestad, 1997; Anthoney & Armstrong, 2010; Johnson &

Bouchard, 2009). Moreover, these studies have often been hindered by the use of

college samples with restricted ability ranges, and failure to separate specific abilities

from general intelligence statistically and conceptually (Johnson & Bouchard, 2009).

For example, Ackerman & Heggestad (1997) reported meta-analytic associations of

interests with nominally specific abilities, but these ability measures were not

statistically independent of g, leaving it unclear to what degree the associations were

ascribable to g or specific abilities. At the same time, general theories of interest-

ability interaction such as Hogan’s and Gottfredson’s have not provided detailed

hypotheses on the overlap between specific abilities and interests, predictions which

might be the most useful for practical and theoretical reasons. In spite of the

limitations of Ackerman and Heggestad’s (1997) meta-analysis, PPIK theory

(intelligence-as-process, personality, interests, and intelligence-as-knowledge) by

Ackerman (1996) did address this specific overlap.

Ackerman and colleagues proposed that intelligence, personality and interests

coalesce into four “trait complexes” (Social, Clerical/Conventional, Science/Math,

and Intellectual/Cultural) (Ackerman, 1996; Ackerman & Heggestad, 1997). They

defined trait complexes as similar to Snow’s (1963) aptitude complexes, or

“combinations of levels of some variables which are particularly appropriate for

efficient learning” (p. 120, cited in Ackerman & Beier, 2003a); however, Ackerman

and colleagues focused on attainment of academic knowledge/expertise and practice

of particular occupations rather than the learning processes necessary to reach those

states. Like Hogan & Roberts (2000), they regarded the interaction between interests

and cognitive ability as reciprocal. They proposed that particular abilities and

interests become more strongly related throughout development because the abilities

are suited to success in certain domains, and the satisfaction brought by this success

spurs the interest that motivates further cognitive investment. Trait complexes are

98

thus held to influence subsequently how individuals select and attain particular

occupations through their roles in knowledge acquisition in academic/occupational

settings (Ackerman, 1996).

In PPIK theory, interests are conceptualized in terms of the RIASEC model

of occupational types by Holland (1973, 1997; Realistic, Investigative, Artistic,

Social, Enterprising and Conventional). The RIASEC model is the predominant

model of occupational interests in the literature. It is based on preferences for six

types of work environments that are organized in a hexagonal circumplex, with

adjacent types more closely related in job demands than opposite types. Research on

the RIASEC hexagon has supported this structure for interests and employment types

in the United States (Holland, 1997; Tracey & Rounds, 1993) A significant

limitation is that the model does not address the roles of cognitive abilities in

interests. However, given the consistent associations between RIASEC interests and

cognitive ability measures that Ackerman and Heggestad (1997) found, they used its

framework to formulate PPIK theory (Ackerman, 1996). The RIASEC model is

well-suited to an integrative framework because work environments are important

(though not the only) contexts in which ability and non-ability traits converge

(Armstrong et al., 2008).

PPIK theory also specified that only two of the four trait complexes primarily

involve ability-interest associations; the other two primarily involve personality-

interest associations (Ackerman, 1997). Based on a meta-analysis of five studies,

Ackerman and Heggestad (1997) observed that only three of the six Holland types

were related substantially to cognitive abilities: Realistic interests, defined as

interests in activities involving physical action and motor coordination; Investigative

interests, defined as interests in cognitive problem solving; and Artistic interests,

defined as interests in expression through artistic media (Holland, 1973). Realistic

and Investigative interests were associated, and each was also associated with

general intelligence (intelligence-as-process in PPIK theory), as well as with math

and spatial abilities, relations which formed a Science/Math trait complex (for

example, the meta-analytic correlation of spatial ability with Realistic interests was

.28; Figure 5; Ackerman, 1996). Artistic interests were held to be associated with

99

crystallized or verbal ability (intelligence-as-knowledge in PPIK theory), forming an

Intellectual/Cultural trait complex (e.g. a correlation of .36 between Artistic interests

and verbal ability; Ackerman, 1996). The other two trait complexes (Social and

Clerical/Conventional) showed moderate associations with personality, but only

minor and/or less consistent associations with cognitive abilities.14

While personality

is likely to be important in understanding occupational outcomes in certain domains,

it may be helpful to further the understanding of interest-ability associations

exclusive of personality before attempting to integrate all three sources of individual

differences. The latter was the focus of the current study. As we did not include

personality in our analyses, we anticipated finding only the Science/Math and

Intellectual/Cultural trait complexes.

Ackerman and colleagues found that their trait complexes were moderately to

strongly related to academic knowledge (Ackerman & Rolfus, 1999), selected

university course (Ackerman, 2000), and university course performance (Kanfer et

al., 2010), which are outcomes along the path to vocational choice. However, trait

complexes in these studies were obtained through factor analysis, which may be

inconsistent with how trait complexes have been conceptualized. Factor analysis is

used to group variables, and relies on the assumption that the groupings apply in the

same way to all individuals in the population. A method of analysis that is arguably

better suited to identifying trait complexes is latent class analysis (LCA). LCA

groups individuals together based on their scores of a set of variables, ignoring the

associations among the variables at the population level. Thus, latent classes can

represent groups of individuals who have “combinations of levels of some variables”

(Snow, 1963), rather than sets of positions on groups of variables.

One study that used LCA to define interests groups was conducted by

Johnson and Bouchard (2009). They examined the mean-level differences in

cognitive abilities among eight latent interest classes, where cognitive ability was

defined according to an updated version of Vernon’s intelligence model, the Verbal,

14

The Conventional trait complex involved a positive correlation between Conventional interests and

perceptual speed (e.g. .15 in Rolfus & Ackerman, 1996), and the Social trait complex small negative

correlations between Social interests with math ability (-.21 in Rolfus & Ackerman, 1996) and spatial

ability (-.13 in Randahl, 1991).

100

Perceptual and Image Rotation (VPR) model (Johnson & Bouchard, 2005b). Based

on previous research in the same sample, Johnson & Bouchard (2009) separated

intelligence into orthogonal factors for general intelligence (g) and two residual

dimensions: Verbal-Image Rotation and Focus-Diffusion (Johnson, Bouchard, et al.,

2007). Mean levels of g varied strongly among the eight latent classes. Beyond this,

however, latent classes of interests in leadership, exploration and adventure were

related to Image Rotation abilities, whereas interests in cultural and persuasion

occupations were related to Verbal abilities. These results were consistent with

Vernon’s (1961) conceptualization of interests in his verbal-perceptual model of

intelligence. Vernon proposed that verbal and math abilities were related to

achievement and interest in traditional educational (math and verbal subjects). On

the other hand, perceptual (spatial and mechanical) abilities were related to aptitude

for technical, scientific and practical subjects. Although Ackerman and Heggestad

(1997) did not statistically isolate g from specific abilities, these two themes were

apparent in their Math/Science and Intellectual/Cultural trait complexes. In addition,

Johnson and Bouchard’s (2009) results supported the important role of g in

occupational interests (Gottfredson, 1986, 2005), such that mean levels of

intelligence varied strongly across the interest classes, being highest for Science and

lowest for Personal Care. This role for g is not as central in the trait complexes of

PPIK theory.

Johnson and Bouchard’s (2009) study was not intended to test the concept of

trait complexes. In order to examine whether there are trait complexes of interests

and abilities, both variables should be entered simultaneously into latent class

analysis, so that groups with different levels of interests and abilities may be

identified. These groups would then reflect the integration of interests and abilities

in interlocked transactions implied by Ackerman and Beier’s (2003) definition of

trait “trait complex”. The main purpose of the current study was to test the validity

of the trait complex concept. This was done by comparing the ability of trait

complexes to predict occupational type with the individual scale scores for cognitive

ability and interests. We hypothesized that if trait complexes are true groupings of

individuals which influence the likelihood of acquiring specialized occupational

knowledge, and thus influence career choices (Ackerman & Beier, 2003a), then

101

latent classes representing them should show strong predictive validity for type of

future employment. Ackerman and Beier (2003) put this question similarly: “is there

a synergy among elements within the trait complexes, so that concentrating on trait

complexes is more informative in the career choice context than individual trait

measures?” (p. 209). Due to the prominent position that trait complexes occupy in

PPIK theory, we predicted that they should demonstrate predictive power (in terms

of explained variance) at least equal to the use of individual scores for cognitive

abilities and interests. Equality would be accepted as being in favour of trait

complexes because of the greater parsimony of latent classes. As a point of

comparison with the research of Ackerman and colleagues, we also compared the

predictive validity of factor-analytic trait complexes to the individual scale scores,

with the same predictions for these trait complexes as for the LCA trait complexes.

The current study was thus meant to address two questions: first, would

exploratorily-derived trait complexes of interests and abilities, obtained through LCA

and factor analysis, replicate the content of the Science/Math and

Intellectual/Cultural trait complexes proposed by Ackerman and Heggestad (1997)?

The Social and Clerical/Conventional trait complexes were not anticipated due to the

exclusion of personality variables in the present analysis. Second, would the trait

complexes obtained by either method display predictive validities at least equal to

those of individual scale scores? The predictions of PPIK theory would be

contradicted if the trait complexes obtained did not resemble the Science/Math and

Intellectual/Cultural trait complexes, and/or if the trait complexes did not display

predictive validities comparable to those of individual scale scores. Additionally, if

only the trait complexes derived by factor analysis satisfied these conditions then it

would raise theoretical questions about their definition as combinations of levels of

the variables.

5.1.2 Previous Project TALENT research

For this study, we made use of data from Project TALENT (PT). PT was a

longitudinal study of American high school students meant to investigate their

aptitudes, interests, and backgrounds, and the influences of these variables on

educational and occupational outcomes (Flanagan et al., 1962). During the study, 60

102

aptitude tests and extensive interest scales were given to a sample of over 400,000

American high school students, who were representative of the U.S. student

population. The students were followed up over an 11-year period after high school

and surveyed on their education and occupational experiences.

Two previous have studies have used PT intelligence and interest data to

predict occupational type (Austin & Hanisch, 1990; Humphreys, Lubinski & Yao,

1993). Austin and Hanisch (1990) examined the tenth-grade PT sample and found

five discriminant functions that predicted the 12 occupation categories defined by PT

investigators. The results indicated that occupational category could be predicted

above chance for 10 of the 12 categories (exceptions were Technical and Sales jobs).

Two major discriminant functions described the interest and ability data. The first

discriminant function, interpreted as verbally-oriented general mental ability, mainly

predicted occupational prestige or level. The second function, which differentiated

individuals based on mathematics, spatial ability and gender, predicted scientific and

technical occupations (Hanisch & Austin, 1990). These functions, however, were

not interpreted in a trait-complex framework, and their capacities to predict

occupation were not compared to those of individual scales.

Humphreys et al. (1993) explored the differential prediction of occupation for

groups defined by the top 20 percentiles of spatial and verbal abilities. They

attempted to equalize these groups for general intelligence by selecting students in

the top 20 percentiles on composites of spatial-math and verbal-math scores, math

ability being used as a proxy for general cognitive ability. They found that the high-

verbal and high-spatial groups had significantly different probabilities of entering

scientific/engineering and humanities jobs. The groups also differed strongly in their

mean occupational interests, with the high spatial-ability group showing greater

interest in mechanical-technical jobs, and the high verbal-ability group more

interested in literary-linguistic jobs. Although these groups could be considered

similar to trait complex groups, they were pre-specified and not derived through any

empirical analysis. In addition, the composite method of Humphreys et al. (1993)

was not entirely successful in controlling for g, as the high verbal group had higher

mean scores for math than the high spatial group (see Table 3 of Humphreys et al.,

103

1993). As in other studies on ability-interest associations, a significant limitation of

both Austin and Hanisch (1990) and Humphreys et al. (1993) was the lack of

independence of the specific ability measures from general intelligence. In

summary, the current study tested the trait complexes of PPIK theory in two novel

ways, by employing latent class analysis to define trait complexes, and by comparing

their predictive validity for future occupation with individual scale scores for

cognitive abilities and interests.

5.2 Method

5.2.1 Sample

The two highest-grade samples of PT were used (grades 11 and 12), because we

considered the older students more likely to have considered their future career

prospects. The use of two samples allowed for replication of potential trait

complexes across samples. In grade 11, there were 47,027 females and 45,292

males. In grade 12, there were 41,456 females and 39,674 males. The total sample

size was 173,449, with 51.0% females. The mean age was 16.4 in grade 11 (SD =

.69) and 17.3 in grade 12 (SD = .67). Males were slightly older than females in both

samples: 16.4 compared to 16.3 in grade 11, and 17.4 compared to 17.2 in grade 12.

The full individual age range was 8 to 21, the younger participants having skipped

multiple grades, and the older participants having been held back.

For the 11-year follow-up mail survey, responses were obtained from 27.5% of

the original grade-11 sample, and 30.9% of the grade-12 sample. In order to adjust

for the lack of representativeness of the follow-up sample, PT investigators

conducted special interviews with non-respondents to the mail questionnaires.

Approximately 2500 participants in each grade cohort were given telephone or in-

person interviews (Wise et al., 1979). Sample weights were created in accordance

with the sampling ratio of the special sample to original the 1960 sample (Wise et al.,

1979). We applied these sample weights to our analyses where follow-up occupation

data were used. The follow-ups were conducted in 1971 for the grade-12 sample and

1972 for the grade-11 sample. Participants were not asked their ages at follow-up;

however the dates of the follow-up surveys were recorded. The mean week of the

year that the surveys were received was week 7.1 in the grade-12 sample (SD = 6.3)

104

and week 8.2 for the grade-11 sample (SD = 8.0). As the baseline data were

collected in March 1960, it can be inferred that the mean follow-up sample age was

approximately ten years and eleven months older than baseline for the grade-12

sample. For the grade-11 sample it was eleven years and eleven months older than at

baseline. Thus, their mean ages were approximately 28.3 for the grade-11 sample

and 28.2 for the grade-12 sample. The follow-up sample was 52% female according

to gender recorded at baseline.

5.2.2 Intelligence measures

The intelligence measures were chosen from the 60 aptitude and achievement

tests in PT (Wise et al., 1979). To ensure that our measures of specific ability did not

rely on overly specialized knowledge, we excluded the “information tests” on

academic and non-academic topics. Our starting point was the 22-test narrow

selection as defined in our previous study (Major et al., 2012; see descriptions of the

tests and reliabilities there). However, we excluded three of the English achievement

tests (Spelling, Capitalization and Punctuation) because they relied too heavily on

knowledge acquired in school classes. The Vocabulary test was also excluded

because it was part of the original information tests. Eighteen tests remained after

this selection. In previous studies, the advanced math test was omitted because it

was designed for students above the tenth grade, and was thus deemed unfair for

younger students. However, it was also excluded here because we initially planned

to use all four grade samples.

Data screening was the same as in Major et al. (2012). Scores on the PT

response credibility index, based on a screening test assessing illiteracy, mental

disability or apathetic testing attitude, were used to exclude participants who did not

reach the cut-offs set by the PT study designers, except where only mental slowness

was indicated (a low score for the number of responses on the Clerical Checking

test). Transformations were applied to two tests that displayed non-normal

distributions (English Usage and Table Reading): English Usage was negatively

skewed, and we applied a square-root transformation (the direction of the variable

was reversed prior to transformation and then reversed back). Table Reading was

strongly positively skewed and leptokurtic and thus had logarithmic and cosine

105

transformations applied. Cases showing severe problems with multivariate outliers

were also removed (Major et al., 2012). Following data screening of the intelligence

tests, total sample size was reduced to 170,723 (1.57% of the total sample removed,

the majority for failure on the response credibility index). After removal of these

cases, some missing data remained for each cognitive ability test. In the male

samples, 2.1 to 2.8% of scores were missing, while 2.1 to 2.7% were missing in the

female samples.

5.2.3 Interest measures

The PT interest scales consist of 17 composites that were designed to capture

interests in different job areas, such as Artistic and Mechanical-Technical jobs (Wise

et al., 1979). However, a limitation of these scales is that they were created on an a

priori basis, without regard to the observed correlations among the items, or to any

particular theoretical framework. Because item-level data were available, we derived

new interest scales based on exploratory factor analysis. Previous studies have

employed the pre-existing scales (J. T. Austin & Hanisch, 1990; Humphreys,

Lubinski, & Yao, 1993). It was anticipated that these new scales would have greater

validity and thus predictive power for occupation than the original PT interest scales

The original interest scales were formed from 205 items, 122 of which were

occupation titles (e.g. musician, rancher, etc.) and 83 of which were activities

applicable to work and school settings (e.g. typewriting, selling furniture). Students

were asked to indicate how well they would like or dislike the occupation or activity,

and were instructed to disregard educational requirements, salary, social standing, or

other factors (Wise et al., 1979). Responses were recorded on a 5-point Likert scale

from “I would dislike this very much” to “I would like this very much”.

For the occupation titles, missing data percentages for the items for each

sample ranged from 2.6-6.7%, and were very consistent across grade and sex. An

exception was 21 consecutive items in each sample that had greater numbers of

omitted responses, likely due to a coding error in the PT database. For example, in

grade-12 males, these 21 items had total missing data percentages of 7.9-9.0%.

Because these additional missing data were missing at random, they should not have

biased the analyses. Three occupation items were excluded from the male samples

106

because they were female-oriented occupations that received very low endorsements

(Maid, Dish Washer and Housewife).

5.2.4 Occupational categories

Table 5.1 contains the occupation category titles and sample percentages for

grade-12 males and females with 11-year follow-up data. Participants were asked to

state their current job titles. These written responses were reduced to 254 job codes

representing specific jobs or job areas such as Airplane Navigator, Veterinarian or

Metal Trades (Wise et al., 1979). These job titles were then organized by PT

investigators into twelve categories according to broad occupational themes. The

most prevalent job category in males was Business Administration, while in females

it was Clerical and Office Work. Due to the period during which the data were

collected, there were large gender differences in the frequency of different

occupational groups.

Table 5.1 Occupation categories and sample percentages (grade 12 sample).

Category title Males (%) Females (%)

Physical Sciences, Engineering and Mathematics 5.5 0.2

Medical and Biological Science 2.5 3.1

Business Administration 19.3 3.1

Teaching and Social Service 8.1 8.3

Humanities, Law, and Social Science 3.4 0.9

Fine and Performing Arts 1.0 0.4

Technical 5.3 1.9

Sales 11.2 1.9

Mechanical and Industrial Trades 8.9 0.5

Construction 7.8 0.02

Clerical and Office Work 3.4 14.7

General Labour and Public Service 15.2 7.6

Vague and Undesignated 8.3 7.9

Housewife N/A 49.5

5.2.5 Method of analysis

The analysis was done in three steps. First, factor analysis was performed

separately on the interest items and cognitive ability scales, and the factor scores

were retained. Second, trait complexes were obtained from the interest and cognitive

ability scores through both factor analysis and latent class analysis, conducted in

Mplus 5.21. In the third step, the trait complex data (class membership and factor

107

scores) were used to predict future occupational category using logistic regression in

Mplus and multinomial regression in SPSS 18. As the first step was preliminary to

the two main analyses, its results are presented in this section.

Missing data were handled through maximum likelihood estimation, which

assumes that the data were missing at random (MAR). This assumption was tenable

because it is unlikely that students purposely avoided particular cognitive ability or

interest tests.

5.2.6 Interest and cognitive ability factors

The 205 items were allocated to 17 original interest scales by PT designers on

an atheoretical basis. We derived new scales based on exploratory factor analysis

(EFA). Two separate EFAs were conducted for the occupation titles and activities.

The analysis was conducted in SPSS with Promax rotation (kappa = 3).

Examining the scree plots in the grade-11 and -12 male samples suggested

seven factors, while it was less clear in the female samples. When further factors

were extracted beyond seven in males, these also displayed adequate simple structure

and interpretability up to the tenth factor. Upon extracting ten factors in females,

nine of these were recognizable counterparts to the male factors, except the tenth,

which was not easily interpretable and obtained no loadings above .35 in either the

grades-11 or -12 samples. Thus, ten interest factors were retained in both males and

females.

The names assigned to the factors in grade-12 males and their two highest

factor loadings were as follows: Trades (Riveter: .77, Bricklayer: .73), Politics (U.S.

Congressman: .98, U.S. Senator: .96), Science (Chemical Engineer: .78, Electrical

Engineer: .76), Business Clerical (Bookkeeper: .75, Office Clerk: .69), Arts (Artist:

.87, Writer: .73), Military (Air Force Officer: .77, Army Officer: .74), Teaching

(High School Teacher: .92, School Principal: ..78), Medical (Doctor: .80, Surgeon:

.79), Business Sales (Stock Salesman: .55, Insurance Agent: .55), and Architecture

(Designer: .49, Architect: .48). The total variance explained by the ten factors was

45.1%.

108

The factors in grade-12 females were assigned the same names as the males;

the following were the two highest loadings in the grade-12 sample: Trades (House

Painter: .72, Deliveryman: .70), Politics (U.S. Congressman: .96, U.S. Senator: .93)

Science (Electrical Engineer: .74, Chemical Engineer: .67), Business Clerical

(Typist: .77, Secretary: .75), Military (Air Force Officer: .83, Marine Corps Officer:

.80), Teaching (High School Teacher: .70, School Principal: .60), Medical (Nurse:

.70, Doctor: .70), Business Sales (Insurance Agent: .55, Personnel Administrator:

.55). The total variance explained by the nine factors was 41.8%.

Factor analysis was also performed on the 83 activity items. Judging by scree

plots and interpretability, six factors were found in the grade-12 male sample.

However, upon examining their contents and correlations with the occupation title

factors, it was found that five out of six of these factors were redundant with the

occupation title factors. For example, there was a factor composed of activities

relevant to trades occupations (e.g. “repair an auto”, “work in a steel mill”), which

correlated highly with the trades factor for the occupation titles (r = 0.76). Activities

factors were found with moderate to high correlations with the occupation factors

that were labelled Science, Business Clerical, Arts, and Business Sales (mean

correlation = .65, SD = .12). The remaining activity factor was composed of Sports

activities, and correlated most highly with the Military occupation factor (r = .35).

Because of the greater robustness of the occupation title factors (due to the greater

number of items), and the redundancy of the activities factors, we decided to retain

only the occupation title factors to represent the occupational interests. The activity

factors were not used.

Due to the presence of missing data, the factor scores for the interest factors

could not be obtained without the exclusion of participants with incomplete data.

Thus, we constructed composites using the uniformly-weighted means of the non-

missing item scores, selecting items that loaded .30 or above on the respective

factors. The composites correlated highly with the factor scores for participants with

complete data. Excluding the Architecture composite, in grade-12 males, the

composites had correlations with their factor scores that ranged from .92 to .99. The

correlation of Architecture composite with its factor was lower (.53). This was due to

109

the lower factor loadings for the Architecture factor (see above). In grade-12

females, the composites had correlations with the factor scores that ranged from .93

to .99. The range of correlations was similar in the grade-11 samples: in grade-11

males it was .78 to .99 (Architecture: .45), in grade-11 females it was .94 to .99.

As in Johnson & Bouchard (2007), we sought to obtain specific cognitive

ability scores that were separate from general intelligence. To do this, we extracted

the general factor of the 18 tests using maximum likelihood estimation and obtained

g factor scores. The g factor explained a mean of 35.6% of the variance in the male

samples, and 36.4% in the female samples. We regressed the individual test scores

onto the g-factor scores, and entered the residuals into EFA in Mplus. In all four

samples, the scree plots suggested four residual factors and the 4-factor EFA solution

displayed good fit statistics. For example, in grade-12 males these were: RMSEA,

.043 (90% confidence interval: .042 - .044), SRMR, .026; in grade-12 females:

RMSEA: .042 (.041- .043), SRMR: .018. The four residual factors were labelled

Spatial (made up of tests requiring spatial reasoning), English (loadings from the

English tests and Memory for Words), Speed (loadings from all the speeded tests),

and Math (a bipolar factor on which the Math tests and Arithmetic loaded positively,

and three Verbal tests loaded negatively).

Based on these exploratory results, we created a confirmatory bi-factor model

in each sample. In the bi-factor model, g is allowed to influence each test score, and

specific abilities form their own factors that are uncorrelated with g. Factor loadings

on the four factors were specified if they were .15 or greater in the EFA results.

Table 2 displays the factor loadings for the confirmatory bi-factor models in the

grade-12 samples. In the grade-12 males the specific ability factors accounted for

2.0% to 9.1% of the variance, in the grade-12 females they accounted for 1.5% to

8.5% of the variance. The same factor model forms were specified in the grade-11

samples, but we allowed the loading parameters to vary freely and did not

specifically test for measurement invariance. Model fit was good in all samples.

The fit statistics in each sample were as follows: grade-11 males: CFI: .983,

RMSEA: .036 (.036 - .037), grade-12 males: CFI: .980, RMSEA: .040 (.040 - .041),

grade-11 females: CFI: .984, RMSEA: .034 (.034 - .035), grade-12 females: CFI:

110

.982, RMSEA: .037 (.036 - .038). Factor scores for g and the specific abilities were

saved for further analyses.

Table 5.2 Factor loadings for grade 12 males/females in the confirmatory intelligence model.

Test Name Factor

g Spatial English Speed Math

Memory for sentences .27/.35

Memory for words .49/.55 .13/.09

Disguised words .62/.64 .22/.20 .30/.31 -.16/-.15

English usage .62/.64 .41/.39

Effective expression .58/.57 .31/.24

Word functions in sent. .73/.77 .13/.06

Reading comprehension .84/.87 .11/– -.18/-.18

Creativity .71/.68 .17/.17 -.22/-.14

Mechanical reasoning .62/.62 .53/.44

Visualization in 2D .40/.41 .46/.42 .23/.18

Visualization in 3D .53/.55 .58/.54

Abstract reasoning .67/.68 .31/.28

Math 1 .79/.77 .17/.19

Math 2 .83/.73 .34/.39

Arithmetic comp. .56/.55 .34/.31 .32/.31

Table reading .22/.24 .70/.70

Clerical checking .08/.09 .73/.71

Object inspection .13/.21 .29/.25 .60/.57

Note: All freely-estimated factor loadings are shown and significant (p < .001)

111

5.3 Results

5.3.1 Factor-analytic trait complexes

Table 5.3 displays the correlations between the abilities and interests in the

grade-12 samples. They reveal that most of the interest composites were positively

related to g, except those scales relating to non-professional or semi-skilled jobs,

which had negative correlations (Trades and Clerical interests in males, and Clerical

interest in females). The scores for residual abilities displayed more differentiated,

and generally much lower, correlations with interest scales. Two notable correlations

were between Spatial ability and Science interest, and between English ability and

Arts interest. Although g and the residual abilities were orthogonal in the

intelligence model, their factor scores had slight non-zero correlations, some of

which were as large as those between interests and abilities.15

15

We removed the small amount of remaining g variance from the residual ability scores through

regression and re-ran the factor and latent class analyses. This did not substantially alter the

characters of the trait complexes obtained through either one.

112

Table 5.3 Correlations matrix for interest composites and cognitive ability factors (grade 12 males/females)

Trades Politics Sci. Cler. Med. Arts Teach. Milit. Sales Arch. g Spatial Eng. Speed Math I: Trades – .28 .49 .25 .23 .32 .23 .46 .37 – .03 .10 -.06 -.06 .01 I: Politics .07 – .44 .12 .31 .50 .49 .41 .57 – .17 -.06 -.02 .01 .07 I: Science .26 .30 – .01 .59 .45 .32 .42 .38 – .30 .09 -.08 -.04 .14 I: Clerical .33 .40 .21 – -.08 .05 .18 .13 .51 – -.20 .02 -.04 .11 .03 I: Medicine .02 .40 .47 .24 – .31 .30 .29 .23 – .16 .00 -.02 -.02 .08 I: Arts .14 .47 .34 .35 .40 – .49 .36 .53 – .31 .01 .06 -.04 -.04 I: Teaching .14 .53 .24 .45 .38 .54 – .27 .50 – .22 -.09 .03 .00 .13 I: Military .28 .36 .42 .23 .29 .29 .26 – .45 – .10 .01 -.03 -.02 .01 I: Sales .19 .63 .29 .67 .41 .56 .56 .40 – – .10 -.04 -.02 .02 .03 I: Architecture .33 .34 .49 .34 .31 .63 .31 .35 .45 – – – – – – g -.26 .14 .25 -.08 .17 .14 .11 .09 .08 .10 – .06 .07 -.01 .16 Spatial .12 -.14 .14 -.13 -.07 -.04 -.16 .01 -.18 .13 .08 – -.42 .12 -.03 English -.12 .05 -.11 .00 .04 .13 .09 -.01 .09 -.04 .13 -.34 – -.07 -.12 Speed -.04 .07 .00 .08 .05 .00 .03 .02 .06 .01 .02 .04 -.06 – -.08 Math -.05 .06 .10 .11 .03 -.07 .07 -.03 .04 -.02 .05 -.25 -.21 -.07 –

Note: Females are above the diagonal, males are below

113

We examined exploratory factor solutions for the interests and cognitive

abilities with additional factors one at a time to assess their fit and interpretability,

beginning from the one-factor solution. Model fit improved markedly from one to

three factors in all samples. Fit also improved from three to four factors, but the

four-factor solutions contained significant problems in both males and females. The

fourth factor in the grade-12 males was a near-singlet factor with Arts interest

loading above 1, and the four-factor solution did not converge in grade-12 females.

Thus, the three-factor EFA solution was used as a basis for constructing a CFA

model for the trait complexes, in conjunction with modification indices. Tables 5.4

and 5.5 display the standardized factor loadings for the CFA model in males and

females, respectively. As the aim of the trait complex model was to capture

covariance between interests and cognitive abilities, correlated residuals were

allowed if they were within the same domain (i.e. within interests or within cognitive

abilities). In the male samples there were positive correlated residuals between

Medicine and Science interests, Sales and Clerical interests, Architecture and Arts

interests, and Verbal and English residual abilities. There were negative correlated

residuals between English and Spatial ability. In the female samples there were only

two correlated residuals, positive between Military and Trades interests, and a

negative residual between English and Spatial ability. The fit statistics of the

models were only very marginally acceptable. They were as follows in each sample:

grade-11 males: CFI: .919, RMSEA: .075 (.073 - .077), grade-12 males: CFI: .914,

RMSEA: .075 (.074 - .076), grade-11 females: CFI: .907, RMSEA: .073 (.071 -

.075), grade-12 females: CFI: .908, RMSEA: .073 (.071 - .075).16

We labelled two of the factors People and Things in males and females, while

the last factor was labelled Trades in males and Clerical in females. The labels

‘People’ and ‘Things’ were inspired by Prediger (1982), who provided evidence of

two underlying dimensions in the RIASEC hexagon, one of which was termed

‘People/Things’, contrasting Social interests (People) with Realistic interests

(Things).

16

MacCallum, Browne and Sugawara (1996) characterized .080 RMSEA as “mediocre”.

114

Table 5.4 CFA solution of interests and abilities (grade 12 males)

Variable Factor

Trades People Things I: Trades .73

I: Politics .76

I: Science .70

I: Clerical .42 .55 -.17

I: Medicine .49 .10

I: Arts .51 .22

I: Teaching .71

I: Military .45

I: Sales .13 .78

I: Architecture .15 .62

g -.72 .66

Spatial -.47 .45

English -.26 .19

Speed .06

Math -.11 .07

I = interest scale score. All freely-estimated factor loadings are

shown and significant (p < .001).

In comparing the three trait complexes to those of PPIK theory, the factor

labelled ‘People’ resembled the Intellectual/Cultural trait complex and the ‘Things’

factor resembled the Science/Math trait complex. The People factor had loadings

from Artistic interests and the residual English (Verbal) ability as in Ackerman

(1996). However, in males particularly there was a greater emphasis on interest

scales reflecting Social or Enterprising interests in RIASEC terms (loadings for

Politics, Teaching and Sales) than would be predicted for the Intellectual/Cultural

trait complex. Therefore, this factor appeared to reflect a broader orientation towards

occupations involving interaction with other people (and was thus labelled the People

factor). The Things factor had loadings from Science interest and Spatial ability that

were consistent with the Science/Math trait complex (Ackerman, 1996). In the

males, Spatial ability had the highest positive loading of any residual ability.

However, this factor also appeared to be somewhat broader in scope than the

Science/Math trait complex, particularly in females where Trades interest also loaded

115

moderately on this factor, consistent with an orientation toward jobs involving

manipulation of the physical world (aligning with the Things pole of Prediger’s

dimension). The moderate loading of Medicine interest on the Things factor in

females may seem to contradict this interpretation. However, Science interest had a

loading of near-unity on the factor, and the highest correlation of Science interest

was with Medicine interest (r = .59); thus, the loading for Medicine may have been at

least partly an indirect effect of Science interest. This factor also received lower

loadings from Math ability than in PPIK theory (Ackerman, 1996).

Table 5.5 CFA solution of interests and abilities (grade 12 females)

Variable Factor

Clerical People Things I: Trades .27 .50

I: Politics .73

I: Science .99

I: Clerical .83

I: Medicine .61

I: Arts .72

I: Teaching .64

I: Military .41 .19

I: Sales .46 .67

I: Architecture

g -.31 .37

Spatial -.18 .20

English .10 -.14

Speed .09

Math .14

I = interest scale score. All freely-estimated factor loadings are

shown and significant (p < .001).

The final factor did not resemble the trait complexes proposed by Ackerman

and colleagues, but instead seemed to relate to occupational prestige or level of

general intelligence. The loadings of g on these factors were moderately negative in

females, and strongly negative in males. The male factor also obtained a negative

loading from English ability. The factor was named ‘Trades’ in males and ‘Clerical’

in females due to these being the highest–loading interest scales. Trades and Clerical

were occupational interest categories requiring less skilled work and which had

116

lower prestige than the other categories. Although not shown, the structure of the

trait complexes was highly similar in the grade-11 samples. The only notable

difference was that in the grade-11 samples the factor loadings were marginally

lower and the latent classes were slightly less distinct. However, all factor loadings

were close in magnitude to those in the grade-12 samples. In males, the loading with

the largest difference from grade 12 was for g on the Trades factor, which was .14

more positive (loading = -.58 in grade 11). In females, the largest difference was for

the loading of Sales on the Clerical factor, which was .05 less positive (loading = .41

in grade 11).

5.3.2 Latent class trait complexes

Latent class analysis was applied to the same interest and cognitive ability

scores. The number of classes was decided by examining the changes (decreases) in

Akaike information criterion (AIC; Akaike, 1983) and Bayesian information

criterion (BIC; Adrian E Raftery, 1995) of the models as additional classes were

added, as well as by considering the classification quality metric of entropy

(Ramaswamy, DeSarbo, Reibstein, & Robinson, 1993).

Examination of the AIC and BIC values showed that they exhibited an

“elbow”, or levelling off, at five classes in the male samples, and at six classes in the

female samples. At this number of classes entropy values were also remained

acceptable (close to .80), and the probabilities for most likely class membership were

.79 or greater for every class. Thus, We decided to retain five classes in males and

six in females. Entropy values were as follows in each sample: grade-11 males

(0.762), grade-12 males (0.761), grade-11 females (0.773), grade-12 females (0.767).

Tables 5.6 and 5.7 display the mean standardized values for the interests and

cognitive abilities in each latent class in grade-12 males and females, respectively.

The latent class means were highly similar in the grade-11 samples, and furnished

the same interpretations of the classes; thus they are not shown. The classes varied

widely in their mean scores, but the most notable pattern was that two classes

contained either people with low occupational interests on all scales (Class 1), or

people with high interest on all scales (Class 5). Moreover, mean g scores were

below average in the low-interest class, whereas g level was above the mean in the

117

high-interest class (females) or at mean level (males). This finding was a recurrence

of the positive correlation of most of the interest scales with g, and the factor-analytic

trait complex that related g to occupational level or prestige. The remaining classes

resembled the factor-analytic trait complexes of People (Class 3 in males, Class 6 in

females) and Things (Class 4 in males and Class 3 in females), but this distinction

was generally less clear than in the factor-analytic trait complexes. The male results

also provided a clearer separation of these two classes than the female samples. As

in the factor-analytic trait complexes, the Spatial and English residual abilities

sometimes showed an opposing pattern; for example, in grade-12 males Spatial

ability was above the mean for the Things-oriented class, but below the mean for the

People-oriented class. The Science/Math and Intellectual/Cultural trait complexes

could be identified with the Things and People-oriented classes, but as in the factor

analysis their interest associations were broader than would be anticipated based on

PPIK theory (Ackerman & Heggestad, 1997).

Table 5.6 Latent class means from LCA (grade 12 males)

Variable Latent class

1 2 3 4 5 I: Trades -.78 -.42 .17 .43

I: Politics -1.73 -1.03 .41 .31 1.46

I: Science -1.62 -.18 -.91 .61 .76

I: Clerical -1.60 -.72 .47 .17 1.15

I: Medicine -1.32 -.49 -.21 .37 .89

I: Arts -1.87 -.80 -.23 .42 1.54

I: Teaching -1.55 -.91 .42 .18 1.46

I: Military -1.46 -.24 -.34 .36 .74

I: Sales -2.84 -1.28 .73 .40 1.97

I: Architecture -2.02 -.39 -.73 .61 1.11

g -.45 .24

Spatial .31 -.90 .29 -.26

English -.17 .46

Speed

Math -.20

Note: Class means between -.14 and .14 not shown. Composition of sample: class 1 =

8.4%, class 2 = 26.0%, class 3 = 15.1%, class 4 = 34.4%, class 5 = 16.0%.

118

Table 5.7 Latent class means from LCA (grade 12 females)

Variable Latent class

1 2 3 4 5 6

I: Trades -1.04 -.35 2.33 -.46 2.32

I: Politics -1.38 -.38 -.50 2.02 1.69

I: Science -1.27 -.60 1.06 .72 1.89 .80

I: Clerical -.46 .60 .27 -1.12 .90

I: Medicine -.92 -.47 .32 .86 .96 .58

I: Arts -1.33 -.16 .38 1.25 1.02

I: Teaching -1.07 1.25 1.02

I: Military -.95 -.22 .77 -.20 1.47 .55

I: Sales -1.74 .43 .38 -.99 2.05 1.19

g -.19 .33 .92 .42 .82

Spatial .40 .19

English -.20 -.20

Speed

Math .26 .15 .20

Note: Class means between -.14 and .14 not shown. Composition of sample: class 1 =

21.4%, class 2 = 25.8%, class 3 = 12.0%, class 4 = 13.3%, class 5 = 7.6%, class 6 = 19.8%.

5.3.3 Prediction of occupational type

In the last stage of the analysis, the cognitive ability and interest scores were

used to predict the occupational type of participants eleven years after high school.

Tables 5.8 and 5.9 display the grade-12 results for the odds ratios for the logistic

regressions of the individual scores and factor-analytic trait complexes (entered in

two separate analyses). All predictors were standardized, hence the odds ratios

represent the increase/decrease in the odds of attaining the particular occupation type

given a one standard deviation increase in the variable. Due to the large sample

sizes, confidence intervals for odds ratios were very small and are not shown.17

To

examine the predictive validity of the individual scores and trait complexes, the sizes

of the odds ratios and pseudo R2 values were compared. The results for the grade-11

samples were similar and not shown.

17

For example, in the prediction of science jobs in grade-12 males, the mean 95% confidence interval

occurred from .022 below the odds ratio estimate (SD = .017) to.023 above the estimate (SD= .017).

119

The odds ratios for the individual scales were consistent with the previous

research on the predictive validity of cognitive abilities and interests (Schmidt &

Hunter, 2004; Wai et al., 2009). According to the odds ratios, the strongest cognitive

predictor for most categories was general intelligence, but the residual abilities also

made some notable contributions. For example, the English residual ability had

strong associations with the Fine Arts categories in both males and females. The

residual abilities also showed discriminative predictive validity, where higher Spatial

ability, for example, contributed positively to scientific and technical jobs, but led to

lower probabilities of attaining social-oriented jobs (such as Teaching, and

Humanities in males). The interest composites also showed good discrimination

among categories; for example, interest in Teaching was strongly predictive of

attaining a job in that category, but negatively related to attaining jobs in several

other categories.

The odds ratios for the trait complexes were generally consistent with their

effectiveness in capturing the shared variance between the interests and cognitive

abilities. The Things factor had large effects on the probabilities of entering in

scientific and technical jobs, whereas the People factor affected jobs in social-

oriented categories. Greater scores on the Trades and Clerical factors decreased the

odds of being in the professional job categories, and increased the odds of being in

the non-professional and semi-skilled job categories (such as Construction jobs in

males, and Clerical jobs in females).

120

Table 5.8 Odds ratios of abilities and interests predicting job categories (grade 12 males).

Predictor Job Category

Science Med. Business Teaching Humanities Fine Arts Technical Sales Mech. Clerical Construc. Labour

g 3.71 3.78 1.25 1.49 2.66 .81 .72 .61 .48

Spatial 1.32 .81 .69 1.34 .83 1.23

English .76 1.20 1.67

Speed 1.45 .75 1.46

Math 1.39 1.63 .70 .82 .83

I: Trades .65 .66 .64 .86 .82 1.55 1.88 1.51

I: Politics 1.21 1.30 1.17 .82

I: Science 2.41 .59 .72 .84 1.49

I: Clerical 1.19 .63 1.36 1.42 1.79

I: Medicine 2.92 .85 .68 .82

I: Arts .76 .83 1.17 1.80 1.22 .68 .82 1.36 1.22

I: Teaching .84 .79 2.66 1.19 .52 .82 .82 .80 .76 .72

I: Military 1.19 .79

I: Sales .74 1.28 1.45 .83 .61 1.38 .82

I: Arch. 1.21 .84 .80

F1: Trades .27 .19 .58 .80 .20 .47 .70 2.52 1.66 3.46 3.87

F2: People .44 1.37 2.33 2.28 .59 .43 1.20 .66

F3: Things 5.72 2.97 1.40 .67 1.48 1.60 1.89 .54 .65 .29

R2: full .52 .47 .11 .25 .42 .21 .10 .06 .22 .15 .26 .25

R2: factors .37 .39 .11 .15 .46 .11 .06 .00 .29 .06 .29 .32

Note: Odds ratios between .86 and 1.14 not shown. R2: full was for individual scales as predictors; R

2: factors was for the trait-complex factors. Med.

= Medicine, Mech. = Mechanical.

121

Table 5.9

Odds ratios of abilities and interests predicting job categories (grade 12 females).

Predictor Job Category

Science Med. Business Teaching Humanities Fine Arts Technical Sales Mech. Clerical Construc. Labour

g 3.85 1.94 1.74 2.97 .64 1.59 .70 .58 .41 .50

Spatial 1.17 .82 1.18 1.16

English 1.23 1.17 1.49 1.35 1.26 1.17 1.35

Speed 1.27 1.35 .78 1.19

Math 2.13 1.25 1.28 1.46 .67 .75 .85 .24 .77

I: Trades .61 .85 1.18 .27 1.32 .39 1.23

I: Politics 1.30 1.28 1.24 1.70 .85 .75 .75 .43

I: Science 3.92 1.31 1.20 .78 1.73 1.58

I: Clerical .66 .47 .56 2.17 .75 1.35 .47

I: Medicine .58 3.22 .64 1.32 .79 .63

I: Arts .55 .84 1.13 1.50 3.31 1.26 1.19 .74

I: Teaching .85 .79 .64 1.89 .82 .73

I: Military 1.13 .76 1.45 1.19 .69 1.52

I: Sales .81 .85 1.64 1.72 .75 1.35 .90 1.20 2.07

F1: Clerical .43 .46 1.52 .40 .28 .28 1.57 1.42 1.61 .56 1.33

F2: People .65 3.25 6.62 7.21 .60 .39 .70 .40

F3: Things 5.71 2.84 1.18 .46 .35 2.16 .65 1.77 .53 1.61

R2: full .55 .39 .13 .29 .45 .52 .21 .13 .19 .08 .63 .18

R2: factors .45 .30 .05 .29 .42 .42 .11 .07 .13 .06 .30 .11

Note: Odds ratios between .86 and 1.14 not shown. R2: full was for individual scales as predictors; R

2: factors was for the trait-complex factors. Med.

= Medicine, Mech. = Mechanical

122

The variances explained by the individual scale scores and the trait

complexes were equal or nearly equal for some categories (such as Humanities in

males and females, and Teaching in females), but were generally lower for the trait

complexes. For the grade-12 males, the mean pseudo-R2

for the trait complexes was

21.8% (SD = .15), compared with 25.2% (SD = .15) for the full scores. In females it

was 22.6% (SD = .15) compared with 31.2% (SD = .19) for the scale scores.

Therefore, our hypothesis that the trait complexes would show predictive validity

equal to the individual scores was not supported. The same conclusion was drawn

for the grade-11 results (see Supplemental Tables C1 and C2).

Tables 5.10 and 5.11 display the odds ratios from logistic regressions of the

jobs categories onto latent class memberships in grade-12 males and females. The

reference class was chosen as the largest group (class 4 in males and class 2 in

females). The odds ratios in the male data were generally smaller than for the

individual scores or factor-analytic trait complexes. However, in grade-12 females,

several large odds ratios were observed for the probabilities of attaining Science,

Medicine and Fine Arts jobs. This was likely due to the small frequencies of jobs in

these categories for females, such that those who attained them were outliers in

interests and abilities. The mean pseudo-R2 for the LCA trait complexes was 6.3%

(SD = .04) for grade 12-males, and 13.7% (SD = .09) for the grade-12 females.

These values were considerably lower than the variance explained by the scale scores

(the same pattern occurred in the grade-11 samples; see Supplemental Tables C3 and

C4). The higher explained variance in females was likely partially attributable to the

use of one additional class. The hypothesis of equal predictive validity was clearly

rejected for the latent-class trait complexes.

123

Table 5.10 Odds ratios of latent classes in predicting job category (grade 12 males).

Predictor Job Category

Science Med. Business Teaching Humanities Fine Arts Technical Sales Mech. Clerical Construc.

a

Labour

Class 1 .33 .16 .37 .64 .39 1.89 .76 1.80 .31 1.68 2.96

Class 2 .69 .41 .47 .74 .80 1.33 1.26 1.54 2.05

Class 3 .10 .57 3.60 2.30 .50 .28 .45 1.17 .51 1.37

Class 5 .63 .40 2.10 1.97 1.76 .67 .68

R2 .15 .08 .05 .10 .07 .05 .06 .00 .05 .04 .04 .04

Note: Reference class is class 4. Odds ratios between .86 and 1.14 not shown. Med. = Medicine, Mech. = Mechanical

Table 5.11 Odds ratios of latent classes in predicting job category (grade 12 females).

Predictor Job Category

Science Med. Business Teaching Humanities Fine Arts Technical Sales Mech. Clerical Construc.a Labour

Class 1 3.90 1.57 .52 .78 .34 3.36 .44 3.24 .73 n/a 1.99

Class 3 9.56 2.47 2.57 1.41 1.39 1.60 .71 1.25 1.41 .66 n/a 2.27

Class 4 15.60 15.11 .35 2.40 1.58 3.38 .29 .12 2.91 .46 n/a

Class 5 11.89 6.26 .77 2.82 1.36 .32 1.34 n/a

Class 6 18.05 4.90 1.16 2.94 2.83 25.52 .39 .61 n/a .24

R2 .29 .20 .09 .08 .13 .28 .06 .14 .08 .02 n/a .14

Note: Reference class is class 2. Odds ratios between .86 and 1.14 not shown. Med. = Medicine, Mech. = Mechanical

a model did not converge.

124

5.3.4 Multinomial prediction

An additional way to predict the occupational categories in Project TALENT

was using multinomial logistic regression, where the binary outcomes of the twelve

categories were predicted simultaneously. An advantage of this method was that the

ability to classify individuals into the correct occupation category could be

determined, comparing across the three different sets of predictors (the individual

scores, the factor-analytic trait complexes, and the latent-class trait complexes).

Austin and Hanisch (1990) similarly examined the classification accuracies of their

five discriminant functions in the grade-10 sample of Project TALENT; therefore our

results can also be compared to theirs.

Tables 5.12 and 5.13 display the correct classification percentages from the

multinomial regressions for the grade-12 males and females (the analysis was not

performed for the grade-11 samples). The percentages are provided for the three

different sets of predictors. The correct classification percentage was greater than

chance when it exceeded the sample percentage (the percentage in the population

who were in that occupation category); hence these values are given for comparison.

The sample percentages differ slightly from those in Table 5.1 because the “Vague

and Undesigned” occupation category was excluded (since it would not be expected

to be able to classify individuals in that category).

The mean classification accuracies are given in the bottom rows. The mean

accuracies indicated that the individual scores had the highest classification accuracy,

followed by the factors and then the latent classes. The classification accuracy

exceeded chance with the individual scores for nine of the twelve categories in males

and six of twelve in females. This accuracy was reduced when the factors or classes

were used as predictors. This finding replicated the results from the logistic

regression that the factors and classes had lower predictive power than the individual

scores for cognitive abilities and interests.

125

Table 5.12

Original sample composition and correct classification percentages for multinomial

regression (grade 12 males)

Classification success (% correct)

Job category Sample

percentage

Individual Scores Factors Classes

Science 6.2 42.0 17.8 0

Medicine 2.8 16.4 0 0

Business 21.3 62.0 74.2 79.0

Teaching 9.1 24.4 1.9 0

Humanities 3.9 16.5 3.0 0

Fine Arts 1.0 0 0 0

Technical 5.7 0.4 0 0

Sales 11.9 9.1 0 0

Mechanical 9.6 20.0 15.5 0

Clerical 8.5 19.8 1.4 0

Construction 3.8 3.0 0 0

Labour 16.2 61.0 61.6 49.1

Mean 8.33 22.9 14.6 10.6

Table 5.13 Original sample composition and correct classification percentages for multinomial

regression (grade 12 females)

Classification success (% correct)

Job category Sample

percentage

Individual Scores Factors Classes

Science 0.4 17.6 0 0

Medicine 7.3 35.3 3.5 0

Business 7.3 0.3 0 0

Teaching 19.2 55.8 42.4 20.2

Humanities 2.1 1.7 0 0

Fine Arts 1.0 27.8 0 0

Technical 4.5 0 0 0

Sales 4.5 0 0 0

Mechanical 1.1 0 0 0

Construction 0.04 0 0 0

Clerical 34.6 74.7 80.0 92.0

Labour 18.0 38.7 17.2 0

Mean 8.33 21.0 11.9 9.4

5.4 Discussion

Cognitive abilities and occupational interests are intertwined, and several

developmental theories have been advanced to explain these associations (Ackerman,

1996; Gottfredson, 1986; Hogan & Roberts, 2000). Only PPIK theory, however, has

provided hypotheses of the overlap between particular interests and abilities, which

are said to be captured by two trait complexes termed Math/Science and

126

Intellectual/Cultural (Ackerman, 1996; Ackerman & Heggestad, 1997). Trait

complexes have been proposed as important influences on occupational knowledge,

and thereby career choice (Ackerman, 1996; Ackerman & Beier, 2003a). Yet,

previous research had not examined whether trait complexes can predict future

occupation. In addition, Ackerman and colleagues have relied on factor analysis to

extract trait complexes, though latent class analysis is arguably more consistent with

their definition. We reasoned that if PPIK theory was correct then trait complexes

obtained from interest and cognitive ability scores in Project TALENT would fulfil

two conditions. First, the content of the Science/Math and Intellectual/Cultural trait

complexes would be replicated, and second, they would show equal or greater

predictive validity than individual scales score for predicting occupational type. The

first condition received only mixed support and the second was not supported; we

discuss each of the hypotheses in turn.

When an acceptable confirmatory factor analytic model of the trait complexes

was constructed, factors were found that resembled the Science/Math and

Intellectual/Cultural trait complexes. However, the involvement of interests in the

trait complexes was broader than proposed in PPIK theory, and the factor content

aligned more closely with the two poles of the People/Things interest dimension

(Prediger, 1982). The People factor had loadings from Sales, Politics and Teaching

interests, which correspond to Enterprising and Social interests in the RIASEC

framework. These interests were not hypothesized to be part of the

Intellectual/Cultural trait complex (Ackerman & Heggestad, 1997), but are

characteristic of the People interest pole (Prediger, 1982). The Things factor

displayed a closer correspondence with the Science/Math trait complex, except that

the loading of residual Math ability was minimal. Johnson and Bouchard (2009) also

found that a broad section of people-oriented interest groups had higher verbal

abilities, while groups that were things-oriented displayed higher spatial (image

rotation) abilities. This also contradicted the more narrow focus of PPIK theory.

However, in this study, the most notable departure from PPIK theory was the

presence of a third factor.

127

The factor captured the negative associations of general intelligence with the

Trades and Clerical interests. Tracey and Rounds (1996) found a third dimension of

the RIASEC interests that is relevant to this finding. The first two dimensions were

defined by Prediger (1982), who labelled them People/Things and Data/Ideas

(Data/Ideas was oriented between Conventional and Enterprising interest (Data) and

Investigative and Artistic interests (Ideas). Tracey and Rounds (1996) performed a

principal component analysis of interest ratings for 229 occupation titles, and found a

third component that was related to occupational prestige or socioeconomic status.

Our third factor was consistent with such a prestige dimension, and specifically

included g, which Tracey and Rounds did not measure. The involvement of g in the

trait complexes was extensive. Considered together, the trait complexes explained

by far the most variance in the g factor. For example, in the grade-12 males, the trait

complexes explained 43.7% of the variance in g, but only 18.9% for the next-highest

ability (Spatial ability). In fact, there was more variance explained in g than in the

four specific abilities combined, which had a total of 27.6% of their variance

explained by the trait complexes. In the grade-12 females, this distinction was even

stronger, with 21.9% of the g variance explained, and 7.2% for the specific abilities

combined. These ratios were similar in the grade-11 samples. In the latent class

analysis, it was observed that classes which reflected greater interests in higher-status

occupations (such as Science or Politics) also had above-average g levels. General

intelligence was notably involved with two of the five LCA trait complexes in males,

and five of six in females, while the specific abilities generally played lesser roles.

In the PT scales, there was a moderate general interest factor, and it was

associated with g, particularly according to the LCA results. Some researchers have

cautioned that the general interest factor is likely a “response set” owing to

acquiescence bias or other methodological factors, and have advised to control for it

(e.g. Prediger, 1982). Some occupational interest scales intrinsically control for

differences in average level of response by ipsatisation. However, other researchers

have suggested that the general interest level could have some substantive

psychological meaning, noting that it correlates positively with Extraversion and

Emotional Stability (see Rounds & Tracey, 1993, and the references therein).

128

The Science/Math and Intellectual/Cultural trait complexes of PPIK theory

emphasize the importance of specific abilities in interest-ability associations, but we

found that g played a more important role in the trait complexes than did specific

abilities. These findings replicated those of Johnson and Bouchard (2009) that there

were substantial differences in mean g level across latent-class interest groups, in line

with their average occupational status. The results lend support to Gottfredson’s

theory of circumscription and compromise, which specifies that g plays a central role

in determining the occupations in which individuals become interested, according to

the levels of education and training required (Gottfredson, 1986, 2005).

PPIK theory specifies the involvement of intelligence-as-process in the

Science/Math trait complex (where intelligence-as-process could be considered

similar to g), but does not propose direct involvement of consideration of social

status or training requirements in the emergence of the trait complexes. One possible

reason for this is that Ackerman and colleagues have used RIASEC measures of

interests, which are limited in their representation of low-prestige occupations (Deng

et al., 2007; Tracey & Rounds, 1996). In contrast, the Trades and Clerical interest

scales derived from PT items were primarily formed from low-prestige occupation

titles. Moreover, as noted in the introduction, Ackerman and colleagues did not

separate g variance from specific-ability variance in measuring cognitive abilities,

and thus were not able to evaluate the roles of g and specific abilities separately in

their trait complexes. In addition, they have often used samples of college students,

which suffer from range restriction of cognitive ability, as well as occupational

interests (Ackerman, 2000; Kanfer, Wolf, Kantrowitz & Ackerman, 2010).

The People and Things factors were consistent with the People/Things

interest dimension. However, the factors were positively correlated, which suggests

that they did not act as poles of one dimension in the current study. This may be

attributable to differences in the interest scales used. Studies that assess the RIASEC

types typically find that the correlations among types vary from moderately positive

to moderately negative (De Fruyt & Mervielde, 1999). In the PT data, all the interest

composites correlated positively. This shared variance between interest scales made

it unlikely to obtain factor-analytic trait complexes would that were negatively

129

correlated with each other, given that the interest scales made up the majority of the

variables entered in the analysis.

One source of the more positive correlations among the PT scales compared

with the RIASEC measures could have been that the RIASEC scales were designed

to emphasize the separation between the types by selecting occupation titles that are

unambiguous representatives, whereas the PT occupation titles were not pre-selected

to fit separate categories. Thus, a substantial portion of each item response was

made up of the student’s general level of occupation interest. Another

methodological factor that could have contributed to this common variance was

acquiescence due to testing fatigue because the participants were young and were

required to complete many scales during the course of the study (Flanagan et al.,

1962). Youth could have contributed substantively to the general interest factor as

well, given that the students may not have been aware of the challenges in different

occupations and thus responded more positively to a wide variety of titles than older

and more knowledgeable respondents would have done. Thus, the data in the current

study were probably not ideal to assess whether People/Things consists of one bi-

polar dimension or two dimensions, although the results did suggest two separate

dimensions. Overall, the People and Things factors that we observed were generally

consistent with Intellectual/Cultural and Science/Math trait complexes, but not

identical to them. While the cognitive abilities generally showed the expected

relations with the two factors, the interest loadings appeared to capture divisions that

were more consistent with Prediger’s People/Things distinction than the distinction

between Cultural and Scientific interests emphasized in PPIK theory.

The structures of the trait complexes using both methods were very consistent

across grades. The structure was slightly less clear structure in grade 11, but this

could have simply resulted from the interests and cognitive abilities being less

developed and differentiated in the younger sample. The trait complexes were less

consistent between genders than grades, but in both groups three factors were

obtained that were recognizable as People, Things and prestige factors. In the latent

class analysis, the numbers of classes obtained for males and females differed by

one, but both sets of classes captured groups that were organized according to

130

occupational level and the People-Things distinction. The prestige factor for females

primarily related g negatively to Clerical interests, while in males it related g

negatively to Trades interests. This difference is consistent with the finding that

males are on average more interested in Realistic-type occupations which Trades fall

under, while females are more interested in Social-type interests, which are relevant

to the Clerical factor (Deng et al., 2007). However, social roles for men and women

were also likely involved in this difference in which lower-prestige occupations they

preferred. In the 1970’s it was very uncommon for women to enter Realistic-type

occupations, and less likely for men to enter Social-type occupations that were

Clerical in nature, compared to other non-Clerical jobs. The limitation of the time

period in regard to sex differences is addressed further below. In summary, the trait

complexes obtained were consistent across samples yet differed from those predicted

in PPIK theory. The present study went beyond previous research in studying the

associations of interests to cognitive abilities, to the prediction of attained

occupation. The individual scores for cognitive abilities and interests had substantial

power in predicting occupational category eleven years after high school, consistent

with previous studies (J. T. Austin & Hanisch, 1990; Humphreys et al., 1993).

However, this was the first study using PT data to find that specific abilities,

independent of g, also predicted some occupations. Most notably, residual Spatial

and English abilities displayed the clearest patterns of prediction, with Spatial ability

predicting scientific and technical jobs and English ability predicting jobs in the

broad Humanities area. Residual Math and Speed abilities were less consistent

predictors, but Math ability, for example, was predictive of future Science jobs in

both males and females.

Trait complexes derived by factor analysis were strong predictors of

occupational type, but, excepting a few occupational categories, they explained less

variance than individual scores for cognitive abilities and interests. This was the

case using both logistic and multinomial regression. The latent-class trait complexes

performed notably worse than the factor-analytic trait complexes in predicting

occupational type.

131

The weaker predictive ability of latent-class trait complexes suggests that trait

complexes, if they exist, should not be conceptualized as groups of individuals who

share similar levels of interest and cognitive ability variables. Rather, there was

greater support for the idea that trait complexes could be conceptualized as capturing

parts of the shared variance between cognitive abilities and interests, where these

variables and the trait complexes are continuous. This shared variance may be the

result of the reciprocal influences of cognitive abilities and interests upon each other

through development, as theorized by a number of researchers (Ackerman, 1996;

Armstrong et al., 2008; Hogan & Roberts, 2000), although direct evidence for this is

still lacking.

One possible limitation of the current study was that the trait complexes that

primarily involve personality-intelligence associations were excluded (the Social and

Clerical/Conventional trait complexes). Nonetheless, Ackerman and Beier (2003)

put forth the general claim that trait complexes are more informative about career

choices than individual scales, which should apply to all their trait complexes. In

addition, previous research on trait complexes has found the Science/Math and

Intellectual/Cultural trait complexes to be the two most important predictors of

specialized knowledge (Ackerman & Rolfus, 1999) and the university course that

students select (Ackerman, 2000). Nonetheless, future research could be done to

address the predictive validity of the Social and Clerical/Conventional trait

complexes for attained occupation.

A second limitation was that the selection of the number of trait complexes

through EFA and LCA was subjective, and guided in large part by their

interpretability. The confirmatory models also displayed marginal fit, although the

use of CFA is an advance over previous studies of trait complexes that have only

used exploratory methods. The difficulty in constructing factor models for the trait

complexes may have been exacerbated by problems with the interest and cognitive

ability measures. As discussed above, the interest scales displayed a positive

manifold, which is inconsistent with research on the RIASEC that has found a

circumplex structure for interests (Armstrong et al., 2008; Holland, 1997). The

amount of variance for which the residual abilities accounted was small, which was

132

at least partly attributable to the modest number of tests for each. This may have

contributed to the weaker involvement of the residual abilities in the trait complexes

compared to g, as well as their lesser predictive validity. However, previous studies

have also found that g has the greatest importance (Gottfredson, 1986; Johnson &

Bouchard, 2009).

Another limitation of research on future occupation is that the power of

prediction is dependent on the job market. If the skills and interests in the population

do not match the requirements for the available jobs, at least some individuals will be

mismatched. In addition, there are social and economic pressures that may act to

lead individuals away from their ideal occupations. The 1970s was a period of

increasing educational opportunity in the United States, but the occupational

opportunities were not as dominated by educational qualifications as in the present

day. For women, strong social expectations about gender roles in the division of

labour were present: nearly half the women at follow-up were housewives, and the

most prevalent paid occupation was in the Clerical and Office Work category.

Gender expectations prevented many women from selecting jobs for which their

cognitive abilities and interests were suited. However, the total predictive validities

of abilities and interests were not substantially lower for women compared with men,

even for male-dominated occupation categories. This was possibly because men

with a wider range of abilities and interests would have obtained these jobs, thus

diluting the predictive validity of the baseline variables. One notable divergence was

that the explained variance was greater for Fine Arts occupations for females than

males, which suggests that gender roles may have also restricted the occupational

opportunities of men. In comparison with the present day, manufacturing and trades

jobs were more prevalent, which provided a niche for more low-g workers; the

predictive validity of g and trades’ interests may be lower in modern samples relative

to PT.

The mean classification accuracy across the twelve categories in Austin and

Hanisch (1990) was 30.5%. Here it was lower, even with the use of full scores

(22.9% in males, 21.0% in females). There were several factors that likely

contributed to this. First, Austin and Hanisch did not separate their sample by

133

gender, and instead entered it as a variable in the discriminant function analysis. As

the genders differed strongly in their frequencies across occupation category (see

Table 5.1), this would have increased their predictive power compared to ours.

Socioeconomic status was also used as a predictor in that study but not in the current

one.

In addition, a limitation of multinomial regression is that unequal proportions

in the outcome variable decreases the prediction accuracy because individuals are

more likely to be classified into the more common prior categories. For example, the

two most common categories in males, Business and Labour, were overrepresented

in the classifications. The grade-10 sample of Project TALENT was more evenly

distributed amongst the twelve categories than the grade-11 or grade-12 samples,

which likely contributed to the greater classification accuracies found by Austin and

Hanisch (1990).

In summary, our first finding was that the Science/Math and

Intellectual/Cultural complexes in PPIK theory could not be closely replicated

because the trait complexes we found were broader in content and gave much more

weight to g. Within the factor-analytic trait complexes, g had the most explained

variance, and one factor related low g to interest in Clerical and Trades occupations

(identified with the prestige dimension of Tracey and Rounds [1996]). These

findings were consistent with the theory of Gottfredsson (1986, 2005) that g acts as

an important filter in occupations according to status level. Neither type of trait

complexes were equal predictors of attained occupation when compared to individual

traits, which calls their theoretical status into question. The greater predictive

validity for factor-analytic trait complexes than latent-class trait complexes suggests

that the Science/Math and Intellectual/Cultural trait complexes, if their definition is

expanded, may be useful summaries of the overlap between cognitive abilities and

interests, but they do not appear to represent discrete groups in the population with

combinations of different trait levels.

134

Chapter 6: Conclusion

The studies in this thesis examined the links among cognitive ability, personality

and occupational interests in Project TALENT. The research built towards testing of

a key component of the integrative framework of Ackerman and colleagues

(Ackerman, 1996; Ackerman & Beier, 2003a; Ackerman & Heggestad, 1997), the

concept of trait complexes made up of cognitive abilities and interests. In this final

chapter, the main results of the studies are summarized, and their implications are

discussed within the context of research that strives to integrate the three domains of

individual differences. Some of the limitations of PT data to address this topic are

discussed, and suggestions for future research are provided.

5.1. Cognitive ability

The study presented in chapter 3 was designed to investigate the

psychometric structure of the cognitive ability tests in PT. Three of the most well-

regarded models were compared: the VPR model, the CHC and the Extended Gf-Gc

models. The VPR model was found to have the best fit to the test data in all samples.

The results provided replication of three previous model comparison studies where

the VPR model outperformed the CHC and Gf-Gc models (Johnson & Bouchard,

2005a, 2005b; Johnson, Te Nijenhuis, et al., 2007). This comparative research has

suggested that the VPR model is the most accurate representation of human cognitive

abilities, thus it follows that this model should be the best suited for understanding

the overlap among cognitive abilities, personality and interests. Nonetheless, the

main purpose of this study in the thesis was to develop a model appropriate for use in

summarizing the cognitive ability measures in PT for further research. The topic of

how the VPR model can contribute to integrative research is discussed in section 5.3

below.

5.2. Personality-intelligence associations

The study presented in chapter 4 investigated linear and nonlinear

associations between general intelligence and personality. The linear associations

that were observed were in line with previous research: g was positively associated

135

with PT scales reflecting Openness to Experience and negatively related to

Neuroticism scales (Ackerman & Heggestad, 1997; Zeidner and Shani-Zinovich,

2011). There was mixed support for the hypothesis of a negative association

between g and Conscientiousness: g was negatively associated with Tidiness, but not

Maturity (Chamorro-Premuzic & Furnham, 2006). Finally, the results supported

Ackerman and Wolf’s (2005) hypothesis that the social potency aspect of

Extraversion is positively associated with g, while social closeness is negatively

associated.

In contrast to most previous studies of nonlinear associations, several

significant quadratic effects of g on personality traits were also found. These

quadratic associations were predicted primarily on the basis of previous research

with gifted (high g) samples (Sak, 2004; Zeidner & Shani-Zinovich, 2011). Three of

the most consistent nonlinear associations were between greater g and greater social

potency (Leadership), lower social closeness (Sociability), and lower scores on the

Tidiness facet of Conscientiousness. Another conclusion drawn from the study was

that the general factor in personality self-ratings may be an important confound to

consider when studying personality-intelligence associations.

These findings can be applied to integrative research. PPIK theory and the

integrative framework of Armstrong and colleagues have only addressed linear

associations between cognitive abilities and personality (Ackerman & Heggestad,

1997; Anthoney & Armstrong, 2010; Armstrong et al., 2008). This limitation is due

to the methods used to identify associations between the three domains: factor

analysis and multidimensional scaling, which only capture linear associations among

variables. However, even within linear associations, neither theory has taken into

account the negative associations of g with Neuroticism and the Tidiness aspect of

Conscientiousness, as well as the differential link of g with the two aspects of

Extraversion. These links, although smaller than the relation between g and

Openness to Experience, could be important in understanding how g transacts with

personality to influence the development of occupational interests. For example,

individuals with higher g scores may be less interested in occupations in the

Conventional RIASEC domain, not just because of the occupations’ lower average

136

prestige, but because Conventional jobs have a high requirement for

Conscientiousness, which includes Tidiness (Armstrong et al., 2008).

Although the methods used by existing theorists have not been suited to

incorporating nonlinear associations, one possible method to do so is to examine

groups selected by extreme g scores. The results from chapter 4 suggest that a group

defined by high g would have outlying scores on the personality traits that

demonstrated nonlinear discontinuity with higher g (higher social potency, lower

social closeness and lower Conscientiousness). Understanding the set of

characteristics that are specific to groups with high g or low g levels could also be

important for an integrative theory. For example, the higher average social potency

of the intellectually gifted may incline them towards Enterprising occupations, but

their lower average social closeness may lead them away from Social occupations

that involve working personally with others. Such influences may be underestimated

if only linear associations between g and personality are considered. This conclusion

only addresses the associations between g and personality traits, but nonlinear

associations may also exist between domain-specific cognitive abilities and

personality traits. In fact, nonlinear associations may exist among all three of the

domains, but this is beyond the scope of the current analyses.

Issues surrounding the “general factor of personality” are also a concern for

integrative research. Across the eight grade and gender samples in PT, this

personality factor displayed a mean correlation of .28 with g, an association which

has also been observed in several other studies (Dunkel, 2013; Irwing, Booth,

Nyborg, & Rushton, 2012; Loehlin, 2011). In such cases, the “lower-order”

personality traits will all tend to be positively correlated with g, though the

associations may be non-significant or negative when the GFP is controlled. Future

research is needed to determine if the association between g and the GFP involves

genuine personality variance or some artifactual source, as the standing of the GFP is

still in question (Chang et al., 2012; Hopwood, Wright, & Brent Donnellan, 2011).

In light of this issue, studies that examine the associations of personality and

cognitive abilities should control for socially-desirable responding, ideally by using

multiple raters instead of social desirability scales, as social desirability scales also

137

contain substantive variance (Paulhus, 2002). The presence of a strong GFP in the

PT personality scales, with no way to control for socially desirable responding, is a

significant limitation of this dataset for integrative research.

5.3. Trait complexes

In chapter 5, the concept of trait complexes of interests and cognitive abilities

was tested by examining their long-term predictive validity. It was found that three

trait complexes obtained by factor analysis had nearly the same predictive power for

occupation as individual scores of interests and cognitive abilities, while trait

complexes obtained by latent class analysis performed substantially more poorly than

either. As latent class analysis is more consistent with the definition of trait

complexes by Ackerman and colleagues (Ackerman & Beier, 2003a; Ackerman &

Heggestad, 1997), this definition was undermined. Instead of being viewed as

combinations of levels of traits that exist in certain groups of the population, trait

complexes could only be defended as reflecting shared variance among continuous

variables. This primary conclusion is in line with the view of Armstrong and

colleagues that the integration of cognitive abilities, interests and personality is to be

best understood by considering the associations among dimensional variables

(Anthoney & Armstrong, 2010; Armstrong et al., 2008).

The People and Things factors that were obtained were broader in their

content than was predicted from PPIK theory (the Science/Math and

Intellectual/Cultural trait complexes). This result was in line with previous findings

by Johnson and Bouchard (2009) regarding the distribution of verbal and spatial

abilities across interest groups. In both that study and in chapter 5, verbal ability was

associated with broadly people-oriented interests, while spatial ability was aligned

with things-oriented interests. Moreover, in chapter 5, I found that factors

representing these overlaps were differentially predictive of technical-scientific

versus artistic-humanities jobs. When considered in the context of the VPR model,

these findings strongly suggest that the Verbal-Image Rotation dimension is aligned

with the People/Things dimension first proposed by Prediger (1982). The

Verbal/Spatial distinction in cognitive abilities may be key to understanding how the

People/Things dimension in interests emerges. Differential success in dealing with

138

verbal and spatial tasks may influence individuals’ future interest levels in People

versus Things occupations because of their different task demands. The

People/Things dimension is defined by interest in working closely with other people

versus working with physical objects and data. However, People-oriented

occupations are more likely to require verbal communication abilities, while Things-

oriented occupations are more likely to require skill at spatial manipulation. Thus,

through perceptions of their own verbal and spatial abilities, and their knowledge of

job activities, individuals are likely to gravitate towards either People or Things-type

occupations.

In addition to the important role of the Verbal-Image Rotation dimension in

the VPR model, Johnson and Bouchard (2007) found that it is the primary dimension

along which sex differences occur in cognitive abilities. This is consistent with its

association with the People/Things dimension, as the sex difference on this

dimension is one of the largest among psychological traits (Armstrong et al., 2011;

Lubinski, 2000). There is some evidence that the sex differences in both interests

and cognitive abilities could be driven by genetic differences that are traceable to

different evolutionary investment strategies for males and females (see Johnson &

Bouchard, 2009, and the references therein). However, in Johnson and Bouchard

(2009), sex differences in occupational interests were found to be larger than those

for specific and general cognitive abilites, suggesting that socialization pressures

may cause a greater separation in interests than would be expected on the basis of

cognitive abilities alone (Johnson & Bouchard, 2009).

Along with its better fit over rival models, the central role for sex differences

in cognitive ability in the VPR model is an important element that supports its use for

integrative research. In contrast, these differences are less well-articulated in

theories for the CHC model (Horn & Blankson, 2005) and Gf-Gc models (McGrew,

2009). For example, in the latest review of CHC theory, sex differences were not

even mentioned (McGrew, 2009). In these models, the distinction between spatial

and verbal abilities is typically confounded with the distinction between fluid and

crystallized factors, which undermines the investigation of sex differences. For

example, tests of crystallized intelligence that rely on general knowledge have often

139

been found to favour males (Keith, Reynolds, Patel, & Ridley, 2008). However, tests

that require specific verbal knowledge, such as spelling and vocabulary, tend to favor

females (Johnson, Bouchard, et al., 2007). Therefore, focusing on crystallized

intelligence rather than verbal intelligence may lead researchers to overlook a salient

sex difference in cognitive ability. PPIK theory still refers to the Gf-Gc model

(Ackerman, 1996), and Armstrong and colleagues have not explicitly adopted any

intelligence theory (Armstrong et al., 2008). The results of chapter 5 suggest that

both theories could benefit from incorporation of the VPR model of cognitive

abilities.

Another crucial oversight in PPIK theory is that it does not include the

prestige dimension in occupational interests and its association with g. In PT, the

involvement of g in the Science/Math trait complex was substantial, as predicted in

PPIK theory, but g was also related to greater overall occupational interests, and

particularly to interests in occupations with higher prestige. The strong association

of g with higher-prestige interests supported the developmental theory of Gottfredson

(Gottfredson, 1986, 2005).

The three “trait complex” factors found in the study tended to support the

RIASEC-based approach of Armstrong and colleagues (Armstrong et al., 2008). As

discussed above, two of the factors appeared to align with the People/Things

dimension that underlies the RIASEC (Armstrong et al., 2008; Prediger, 1982). The

third factor was related to occupational prestige or level, a dimension which has also

been incorporated into their framework (Armstrong et al., 2008; Deng et al., 2007).

However, typical RIASEC measures such as the Vocational Preference Inventory do

not contain enough breadth of occupations to cover the whole prestige dimension

(Deng et al., 2007). In addition, Armstrong and colleagues have used aptitude

ratings instead of objective intelligence tests to assess cognitive abilities. Further

research is needed to replicate the association of this third interest dimension with g.

A limitation of the study in chapter 5 was that personality was not included,

in order to focus on first integrating cognitive abilities with interests. It is possible

that the trait complexes in PPIK theory that involve personality-interest associations

could be found in PT, and that they may display better predictive validity than the

140

trait complexes for interests and cognitive abilities. However, as observed in chapter

4, the PT personality measures have greater limitations for this research than do

those for the interests, because of the lack of item-level data and the presence of a

large common factor.

5.4. Suggestions for future research

The research presented in this thesis has provided several important

indications for future integrative research. First, the studies in this thesis support the

conclusion that the VPR model of cognitive abilities is likely the best existing model

of cognitive abilities for this integrative research. This is due to its better description

of the structure of cognitive abilities, and the strong links between the Verbal-Image

Rotation dimension and the People/Things dimension of occupational interests. In

addition, future studies should separate g from specific abilities in their intelligence

models (i.e. specify them to be uncorrelated factors), as this is the only way to

accurately assess their relative contributions in integrative models.

Second, integrative research could benefit from the examination of nonlinear

personality-intelligence associations, which were responsible for some substantive

personality differences of individuals at high and low g levels. These personality

differences, such as lower social closeness and tidiness, and higher social potency,

could be relevant to the development of occupational interests, and apparently can

only be found if nonlinear models are employed. Another issue that deserves more

attention in research on personality-intelligence associations is the “general factor of

personality”, which may be an important confound in understanding these

associations.

Finally, the concept of trait complexes as originally proposed by Ackerman

and colleagues appears to be untenable (Ackerman, 1996; Ackerman & Beier,

2003a). Based on their predictive validity, the overlap among cognitive abilities,

personality and interests is best conceived as being among continuous variables. One

of the most promising approaches is to use the dimensions that underlie the RIASEC

model to anchor cognitive abilities and personality traits (Anthoney & Armstrong,

2010; Armstrong et al., 2008). The factors of interests and cognitive abilities found

in chapter 5 bore a strong resemblance to the People/Things and prestige dimensions

141

found in the models of Armstrong and colleagues. Hence, integrative models may be

best based around the structure of the RIASEC model of occupational interests,

although further research is needed to compare this approach more directly to PPIK

theory, and to examine its capacity to predict occupational outcomes.

142

References

Ackerman, P. L. (1996). A theory of adult intellectual development: Process,

personality, interests, and knowledge. Intelligence, 22(2), 227-257. doi:

10.1016/s0160-2896(96)90016-1

Ackerman, P. L. (2000). Domain-Specific knowledge as the "dark matter" of adult

Intelligence, Gf/Gc, personality and interest correlates. The Journals of

Gerontology Series B: Psychological Sciences and Social Sciences, 55(2),

P69-P84.

Ackerman, P. L., & Beier, M. E. (2003a). Intelligence, personality, and interests in

the career choice process. Journal of Career Assessment, 11(2), 205-218.

Ackerman, P. L., & Beier, M. E. (2003b). Trait complexes, congitive investment and

domain knowledge. In R. J. Sternberg & E. L. Grigorenko (Eds.),

Perspectives on the psychology of abilities, competencies, and expertise (pp.

1-30). New York: Cambridge University Press.

Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests:

evidence for overlapping traits. Psychological bulletin, 121(2), 219.

Ackerman, P. L., & Rolfus, E. L. (1999). The locus of adult intelligence: Knowledge,

abilities, and non-ability traits. Psychology and Aging, 14, 314-330.

Akaike, H. (1983). Information measures and model selection. Bulletin of the

International Statistical Institute, 50(1), 277-291.

Anthoney, S. F., & Armstrong, P. I. (2010). Individuals and environments: Linking

ability and skill ratings with interests. Journal of counseling psychology,

57(1), 36.

Anusic, I., Schimmack, U., Pinkus, R. T., & Lockwood, P. (2009). The nature and

structure of correlations among Big Five ratings: The halo-alpha-beta model.

Journal of Personality and Social Psychology, 97(6), 1142.

Armstrong, P. I., Day, S. X., McVay, J. P., & Rounds, J. (2008). Holland's RIASEC

model as an integrative framework for individual differences. Journal of

counseling psychology, 55(1), 1-18.

Armstrong, P. I., Su, R., & Rounds, J. (2011). Vocational interests: The road less

traveled. The Wiley-Blackwell Handbook of Individual Differences, 608-631.

Ashton, M. C., & Lee, K. (2012). On Models of Personality Structure. European

Journal of Personality, 26(4), 433-434.

Ashton, M. C., Lee, K., Goldberg, L. R., & de Vries, R. E. (2009). Higher order

factors of personality: do they exist? Personality and Social Psychology

Review, 13(2), 79-91.

Austin, E. J., Deary, I. J., & Gibson, G. J. (1997). Relationships between ability and

personality: Three hypotheses tested. Intelligence, 25(1), 49-70. doi:

http://dx.doi.org/10.1016/S0160-2896(97)90007-6

Austin, E. J., Deary, I. J., Whiteman, M. C., Fowkes, F., Pedersen, N. L., Rabbitt, P.,

. . . McInnes, L. (2002). Relationships between ability and personality: Does

143

intelligence contribute positively to personal and social adjustment?

Personality and Individual Differences, 32(8), 1391-1411.

Austin, J. T., & Hanisch, K. A. (1990). Occupational attainment as a function of

abilities and interests: A longitudinal analysis using Project TALENT data.

Journal of Applied Psychology, 75(1), 77-86.

Bäckström, M., Björklund, F., & Larsson, M. R. (2009). Five-factor inventories have

a major general factor related to social desirability which can be reduced by

framing items neutrally. Journal of Research in Personality, 43(3), 335-344.

Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job

performance: a meta-analysis. Personnel Psychology, 44(1), 1-26. doi:

10.1111/j.1744-6570.1991.tb00688.x

Barrick, M. R., Mount, M. K., & Gupta, R. (2003). Meta‐analysis of the relationship

between the five‐factor model of personality and Holland's occupational

types. Personnel Psychology, 56(1), 45-74.

Bartholomew, D. (1987). Latent variable models and factor analysis. London,

England: Griffin.

Bartholomew, D. J., Deary, I. J., & Lawn, M. (2009). A new lease of life for

Thomson's bonds model of intelligence. Psychol Rev, 116(3), 567-579.

Benson, N., Hulac, D. M., & Kranzler, J. H. (2010). Independent examination of the

Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV): What does the

WAIS-IV measure? Psychological Assessment, 22(1), 121-130.

Bouchard, T. J. (1997). Experience producing drive theory: How genes drive

experience and shape personality. Acta Paediatrica, 86(S422), 60-64.

Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York,

NY: Guilford Press; US.

Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies.

New York, NY: Cambridge University Press; US.

Cattell, R. B. (1943). The measurement of adult intelligence. Psychological bulletin,

40(3), 153.

Cattell, R. B. (1950). Personality. New York: McGraw-Hill.

Cattell, R. B. (1963). The theory of fluid and crystallized intelligence: A critical

experiment. Journal of Educational Psychology, 54, 1-22.

Cattell, R. B. (1987). Intelligence: Its structure, growth and action. New York:

Elsevier Science Publishing Company, Inc.

Cervone, D. (2005). Personality architecture: Within-person structures and processes.

Annual Reviews of Psychology, 56, 423-452.

Chamorro-Premuzic, T., & Furnham, A. (2006). Intellectual Competence and the

Intelligent Personality: A Third Way in Differential Psychology. Review of

General Psychology, 10(3), 251-267.

Chang, L., Connelly, B. S., & Geeza, A. A. (2012). Separating method factors and

higher order traits of the Big Five: A meta-analytic multitrait–multimethod

144

approach. Journal of Personality and Social Psychology; Journal of

Personality and Social Psychology, 102(2), 408.

Conn, S. R., & Rieke, M. L. (Eds.). (1994). 16PF Fifth Edition technical manual.

Champaign, IL: Institute for Personality and Ability Testing.

Costa Jr, P. T., & McCrae, R. R. (1992). NEO PI-R Professional Manual. Odessa,

FL: Psychological Assessment Resources.

Costa, P. T., & MacCrae, R. R. (1992). Revised NEO Personality Inventory (NEO

PI-R) and NEO Five-Factor Inventory (NEO FFI): Professional Manual:

Psychological Assessment Resources.

Costa, P. T., & McCrae, R. R. (1995). Primary traits of Eysenck's PEN system:

Three-and five-factor solutions. Journal of personality and social psychology,

69(2), 308.

Cramer, A. O., Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S. H., . .

. Borsboom, D. (2012). Dimensions of normal personality as networks in

search of equilibrium: You can't like parties if you don't like people.

European Journal of Personality, 26(4), 414-431.

Cureton, E. E. (1968). A factor analysis of Project Talent tests and four other

batteries. Pittsburgh, PA: American Institutes for Research and School of

Education, University of Pittsburgh.

Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory of work adjustment:

An individual-differences model and its applications: University of

Minnesota Press Minneapolis, MN.

De Fruyt, F., & Mervielde, I. (1999). RIASEC types and Big Five traits as predictors

of employment status and nature of employment. Personnel Psychology,

52(3), 701-727.

De Raad, B., & Barelds, D. P. (2008). A new taxonomy of Dutch personality traits

based on a comprehensive and unrestricted list of descriptors. Journal of

personality and social psychology, 94(2), 347.

De Raad, B., & Peabody, D. (2005). Cross‐culturally recurrent personality factors:

analyses of three factors. European Journal of Personality, 19(6), 451-474.

de Vries, R. E. (2011). No evidence for a General Factor of Personality in the

HEXACO Personality Inventory. Journal of Research in Personality, 45(2),

229-232.

Deary, I. J. (2009). The trait approach to personality G. M. Philip J. Corr (Ed.) The

Cambridge handbook of personality psychology (pp. 89-109).

Deng, C.-P., Armstrong, P. I., & Rounds, J. (2007). The fit of Holland’s RIASEC

model to US occupations. Journal of Vocational Behavior, 71(1), 1-22.

DeYoung, C. G. (2011). Intelligence and personality. In R. J. Sternberg & S. B.

Kaufman (Eds.), The Cambridge handbook of intelligence (pp. 711-737).

New York: Cambridge University Press.

DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2005). Sources of

openness/intellect: Cognitive and neuropsychological correlates of the fifth

factor of personality. Journal of personality, 73(4), 825-858.

145

DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and

domains: 10 aspects of the Big Five. Journal of personality and social

psychology, 93(5), 880.

Dunkel, C. S. (2013). The general factor of personality and general intelligence:

Evidence for substantial association. Intelligence, 41(5), 423-427.

Einarsdóttir, S., & Rounds, J. (2000). Application of three dimensions of vocational

interests to the Strong Interest Inventory. Journal of Vocational Behavior,

56(3), 363-379.

Eysenck, H. J. (1939). Review of Thurstone's Primary Mental Abilities, 1938. British

Journal of Educational Psychology, 39, 188-231.

Eysenck, H. J. (1994). Personality and intelligence: Psychometric and experimental

approaches. In R. J. Sternberg & P. Ruzgis (Eds.), Personality and

intelligence (pp. 3-31). New York: Cambridge University Press.

Eysenck, H. J., & White, P. O. (1964). Personality and the measurement of

intelligence. British Journal of Educational Measurement, 24, 197-201.

Flanagan, D. P., Ortiz, S. O., & Alfonso, V. C. (2007). Essentials of cross-battery

assessment (2nd ed.). New Jersey: John Wiley & Sons, Inc.

Flanagan, J. C., Dailey, J. T., Shaycroft, M. F., Gorham, W. A., Orr, D. B., &

Goldberg, I. (1962). Design for a study of American youth. Boston: Houghton

Mifflin.

Flanagan, J. C., Davis, F. C., Dailey, J. T., Shaycroft, M. F., Orr, D. B., Goldberg, I.,

& Neyman, C. A. (1964). The American high-school student: The

identification, development, and utilization of human talents. Pittsburg:

Project Talent Office, University of PIttsburgh.

Goldberg, L. R. (1990). An alternative" description of personality": the big-five

factor structure. Journal of personality and social psychology, 59(6), 1216.

Gottfredson, L. S. (1986). Occupational aptitude patterns map: Development and

implications for a theory of job aptitude requirements. Journal of Vocational

Behavior, 29(2), 254-291.

Gottfredson, L. S. (2005). Applying Gottfredson's theory of circumscription and

compromise in career guidance and counseling. In S. D. Brown & R. W. Lent

(Eds.), Carreer development and counseling: Putting theory and research to

work (pp. 71-100). New York: John Wiley.

Guilford, J. P. (1959). Personality. New York: McGraw-Hill.

Gustafsson, J.-E. (1984). A unifying model for the structure of intellectual abilities.

Intelligence, 8(3), 179-203. doi: Doi: 10.1016/0160-2896(84)90008-4

Gustafsson, J.-E. (1988). Hierarchical models of individual difference in cognitive

abilities. In R. J. Sternberg (Ed.), Advances in the psychology of human

intelligence (Vol. 4). Hillsdale, New Jersey: Lawrence Erlbaum Associates,

Inc.

Gustafsson, J.-E. (2002). Measurement from a hierarchical point of view. In H. I.

Braun, D. N. Jackson & D. E. Wiley (Eds.), The role of constructs in

146

psychological and educational measurement (pp. 73-95). London: Lawrence

Erlbaum Associates, Publishers.

Hakistian, A. R., & Catell, R. B. (1975). The comprehensive ability battery.

Champaign, IL: Institute for Personality and Ability Testing.

Harring, J. R., Weiss, B. A., & Hsu, J. C. (2012). A comparison of methods for

estimating quadratic effects in nonlinear structural equation models.

Psychological Methods, 17(2), 193-214.

Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical science,

297-310.

Hogan, R. (1983). A socioanalytic theory of personality. In M. M. Page (Ed.),

Nebraska symposium on motivation: Vol. 30. Personality: Current theory and

research (pp. 55-89). Lincoln: University of Nebraska Press.

Hogan, R., & Roberts, B. W. (2000). A socioanalytic perspective on person–

environment interaction.

Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling

Psychology, 6(1), 35.

Holland, J. L. (1973). Making vocational choices: A theory of careers (Vol. 37):

Prentice-Hall Englewood Cliffs, NJ.

Holland, J. L. (1985). Vocational Preference Inventory (VPI) manual: 1985 edition.

Odessa, FL: Psychological Assessment Resources.

Holland, J. L. (1997). Making vocational choices: A theory of vocational

personalities and work environments. Odessa, FL: Psychological Assessment

Resources

Hopwood, C. J., Wright, A. G. C., & Brent Donnellan, M. (2011). Evaluating the

evidence for the general factor of personality across multiple inventories.

Journal of Research in Personality, 45(5), 468-478. doi:

http://dx.doi.org/10.1016/j.jrp.2011.06.002

Horn, J. L. (1998). A basis for research on age differences in cognitive capacities. In

J. J. McArdle & R. W. Woodcock (Eds.), Human cognitive abilities in theory

and practice (pp. 29-73). Mahwah, NJ: Erlbaum.

Horn, J. L., & Blankson, N. (2005). Foundations for a better understanding of

cognitive abilities. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary

intellectual assessment: Theories, tests, and issues (pp. 41-68).

Horn, J. L., & Noll, J. (1997). Human cognitive abilities: Gf-Gc theory. In D. P.

Flanagan, J. L. Genshaft & P. L. Harrison (Eds.), Contemporary intellectual

assessment (pp. 53-91). New York, NY: Guilford Press.

Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance

structure analysis: Conventional criteria versus new alternatives. Structural

Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.

Humphreys, L. G., Lubinski, D., & Yao, G. (1993). Utility of predicting group

membership and the role of spatial visualization in becoming an engineer,

physical scientist, or artist. Journal of Applied Psychology, 78, 250-250.

Hunt, E. B. (2011). Human Intelligence. Cambridge: Cambridge University Press.

147

Irwing, P., Booth, T., Nyborg, H., & Rushton, J. P. (2012). Are g and the general

factor of personality (GFP) correlated? Intelligence, 40(3), 296-305.

Jackson, D. N. (1977). Manual for the Jackson Vocational Interest Survey. Port

Huron, MI: Research Psychologists Press.

Jensen, A. (1998). The g factor. Wesport, CN: Praeger.

Johnson, W., & Bouchard, T. J. (2009). Linking abilities, interests, and sex via latent

class analysis. Journal of Career Assessment, 17(1), 3-38.

Johnson, W., & Bouchard, T. J., Jr. (2005a). Constructive replication of the visual-

perceptual-image rotation model in Thurstone's (1941) battery of 60 tests of

mental ability. Intelligence, 33(4), 417-430.

Johnson, W., & Bouchard, T. J., Jr. (2005b). The structure of human intelligence: It

is verbal, perceptual, and image rotation (VPR), not fluid and crystallized.

Intelligence, 33(4), 393-416.

Johnson, W., Bouchard, T. J., Jr., McGue, M., Segal, N. L., Tellegen, A., Keyes, M.,

& Gottesman, I. I. (2007). Genetic and environmental influences on the

Verbal-Perceptual-Image Rotation (VPR) model of the structure of mental

abilities in the Minnesota study of twins reared apart. Intelligence, 35(6),

542-562.

Johnson, W., Jung, R. E., Colom, R., & Haier, R. J. (2008). Cognitive abilities

independent of IQ correlate with regional brain structure. Intelligence, 36(1),

18-28. doi: DOI: 10.1016/j.intell.2007.01.005

Johnson, W., Te Nijenhuis, J., & Bouchard, T. J., Jr. (2007). Replication of the

hierarchical visual-perceptual-image rotation model in de Wolff and Buiten's

(1963) battery of 46 tests of mental ability. Intelligence, 35(1), 69-81. doi:

http://dx.doi.org/10.1016/j.intell.2006.05.002

Judge, T. A., Higgins, C. A., Thoresen, C. J., & Barrick, M. R. (1999). The big five

personality traits, general mental ability, and career success across the life

span. Personnel Psychology, 52(3), 621-652.

Kan, K.-K., Kievet, R. A., Dolan, C. V., & van der Maas, H. L. (2011). On the

interpretation of the CHC factor Gc. Intelligence, 35(5), 292-302.

Kanfer, R., Wolf, M. B., Kantrowitz, T. M., & Ackerman, P. L. (2010). Ability and

trait complex predictors of academic and job performance: A person–

situation approach. Applied Psychology, 59(1), 40-69.

Kaufman, J. C. (2012). Self estimates of general, crystallized, and fluid intelligences

in an ethnically diverse population. Learning and Individual Differences, 22,

118-122.

Keith, T. Z., Fine, J. G., Taub, G. E., Reynolds, M. R., & Kranzler, J. H. (2006).

Higher order, multisample, confirmatory factor analysis of the Wechsler

Intelligence Scale for Children—Fourth Edition: What does it measure?

School Psychology Review, 35, 108-127.

Keith, T. Z., Reynolds, M. R., Patel, P. G., & Ridley, K. P. (2008). Sex differences in

latent cognitive abilities ages 6 to 59: Evidence from the Woodcock–Johnson

III tests of cognitive abilities. Intelligence, 36(6), 502-525.

148

Kenny, D. A. (2011). Measuring model fit Retrieved February 15, 2012, from

http://davidakenny.net/cm/fit.htm

Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent

interaction effects with the LMS method. Psychometrika, 65(4), 457-474.

Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: how difficulties in

recognizing one's own incompetence lead to inflated self-assessments.

Journal of Personality and Social Psychology; Journal of Personality and

Social Psychology, 77(6), 1121.

Kuder, G. F., & Richardson, M. W. (1937). The theory of test reliability.

Psychometrika, 2, 151-160.

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career

potential, creativity, and job performance: can one construct predict them all?

Journal of personality and social psychology, 86(1), 148-161.

Kvist, A. V., & Gustafsson, J.-E. (2008). The relation between fluid intelligence and

the general factor as a function of cultural background: A test of Cattell's

Investment theory. Intelligence, 36(5), 422-436. doi:

http://dx.doi.org/10.1016/j.intell.2007.08.004

Li, S.-C., Lindenberger, U., Hommel, B., Aschersleben, G., Prinz, W., & Baltes, P.

B. (2004). Transformations in the couplings among intellectual abilities and

constituent cognitive processes across the life span. Psychological Science,

15(3), 155-163. doi: 10.1111/j.0956-7976.2004.01503003.x

Loehlin, J. C. (2011). Correlation between general factors for personality and

cognitive skills in the National Merit twin sample. Journal of Research in

Personality, 45(5), 504-507.

Lohnes, P. R. (1966). Measuring adolescent personality: Project Talent five-year

follow-up studies, interim report 1. Pittsburgh, PA: University of Pittsburgh.

Lubinski, D. (2000). Scientific and social significance of assessing individual

differences:“Sinking shafts at a few critical points”. Annual review of

psychology, 51(1), 405-444.

Luciano, M., Leisser, R., Wright, M. J., & Martin, N. G. (2004). Personality, arousal

theory and the relationship to cognitive ability as measured by inspection

time and IQ. Personality and Individual Differences, 37(5), 1081-1089. doi:

http://dx.doi.org/10.1016/j.paid.2003.11.016

Lykken, D. T., Bouchard, T., McGue, M., & Tellegen, A. (1993). Heritability of

interests: a twin study. Journal of Applied Psychology, 78(4), 649.

Mackintosh, N. J. (2012). IQ and human intelligence (2nd ed.). New York, NY:

Oxford University Press.

Major, J. T., Johnson, W., & Deary, I. J. (2012). Comparing models of intelligence in

Project TALENT: The VPR model fits better than the CHC and extended Gf–

Gc models. Intelligence, 40(6), 543-559.

McCrae, R., & Costa, P. (1989). Reinterpreting the Myers-Briggs Type Indicator

from the perspective of the five-factor model of personality. Journal of

personality, 57(1), 17.

149

McCrae, R. R. (2009). The five-factor model of personality traits: Consensus and

controversy. In P. J. Corr & G. Matthews (Eds.), The Cambridge handbook of

personality psychology (pp. 148-161): Cambridge University Press.

McCrae, R. R., & Costa Jr, P. T. (2012). Personality in adulthood: A five-factor

theory perspective: Guilford Press.

McCrae, R. R., & Costa, P. T. (1985). Updating Norman's" adequacy taxonomy":

Intelligence and personality dimensions in natural language and in

questionnaires. Journal of personality and social psychology, 49(3), 710.

McCrae, R. R., & Terracciano, A. (2005). Universal features of personality traits

from the observer's perspective: data from 50 cultures. Journal of personality

and social psychology, 88(3), 547.

McGrew, K. S. (1997). Analysis of the major intelligence batteries according to a

proposed comprehensive Gf-Gc framework. In D. P. Flanagan, J. L. Genshaft

& P. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests,

and issues (pp. 151-179). New York, NY: Guilford Press; US.

McGrew, K. S. (2005). The Cattell-Horn-Carroll Theory of Cognitive Abilities: Past,

present, and future. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary

intellectual assessment: Theories, tests, and issues (pp. 136-181). New York,

NY: Guilford Press; US.

McGrew, K. S. (2009). CHC theory and the human cognitive abilities project:

Standing on the shoulders of the giants of psychometric intelligence research.

Intelligence, 37(1), 1-10. doi: http://dx.doi.org/10.1016/j.intell.2008.08.004

McLarnon, M. J., & Carswell, J. J. (2012). The personality differentiation by

intelligence hypothesis: A measurement invariance investigation. Personality

and Individual Differences.

Moosbrugger, H., Schermelleh-Engel, K., Kelava, A., & Klein, A. G. (2009). Testing

multiple nonlinear effects in structural equation modeling: A comparison of

alternative estimation approaches. In T. Teo & M. S. Khine (Eds.), Structural

equation modelling in educational research: Concepts and applications.

Rotterdam, The Netherlands: Sense Publishers.

Moutafi, J., Furnham, A., & Paltiel, L. (2005). Can personality factors predict

intelligence? Personality and Individual Differences, 38, 1021-1033.

Myers, I. B., McCaulley, M. H., & Most, R. (1985). Manual: A guide to the

development and use of the Myers-Briggs Type Indicator: Consulting

Psychologists Press Palo Alto, CA.

Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes:

Replicated factor structure in peer nomination personality ratings. The

Journal of Abnormal and Social Psychology, 66(6), 574.

O'Connor, B. (2000). SPSS and SAS programs for determining the number of

components using parallel analysis and Velicer's MAP test. Behavior

Research Methods, Instruments, & Computers, 32(2), 396-402.

Paulhus, D. L. (2002). Socially desirable responding: The evolution of a construct. In

H. I. Braun, D. N. Jackson & D. E. Wiley (Eds.), The role of constructs in

150

psychological and educational measurement (pp. 49-69). Mahwah, NJ:

Erlbaum.

Pincombe, J. L., Luciano, M., Martin, N. G., & Wright, M. J. (2007). Heritability of

NEO PI-R extraversion facets and their relationship with IQ. Twin Research

and Human Genetics, 10(3), 462-469.

Prediger, D. J. (1982). Dimensions underlying Holland's hexagon: Missing link

between interests and occupations? Journal of Vocational Behavior, 21(3),

259-287.

Raftery, A. E. (1995). Bayesian model selection in social research. Sociological

Methodology, 25, 111-163.

Raftery, A. E. (1995). Bayesian model selection in social research. Sociological

methodology, 25, 111-164.

Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An

empirical pooling approach for estimating marketing mix elasticities with

PIMS data. Marketing Science, 12(1), 103-124.

Randahl, G. J. (1991). A typological analysis of the relations between measured

vocational interests and abilities. Journal of Vocational Behavior, 38(3), 333-

350.

Reeve, C. L. (2004). Differential ability antecedents of general and specific

dimensions of declarative knowledge: More than g. Intelligence, 32(6), 621-

652. doi: http://dx.doi.org/10.1016/j.intell.2004.07.006

Reeve, C. L., & Bonaccio, S. (2011). The Nature and Structure of “Intelligence”. The

Wiley-Blackwell Handbook of Individual Differences.

Reeve, C. L., & Hakel, M. D. (2000). Toward an understanding of adult intellectual

development: Investigating within-individual convergence of interest and

knowledge profiles. Journal of Applied Psychology, 85(6), 897.

Reeve, C. L., & Heggestad, E. D. (2004). Differential relations between general

cognitive ability and interest-vocation fit. Journal of Occupational and

Organizational Psychology, 77(3), 385-402. doi:

http://dx.doi.org/10.1348/0963179041752673

Reeve, C. L., Meyer, R. D., & Bonaccio, S. (2006). Intelligence-personality

associations reconsidered: The importance of distinguishing between general

and narrow dimensions of intelligence. Intelligence, 34(4), 387-402.

Rolfus, E. L., & Ackerman, P. L. (1996). Self-report knowledge: At the crossroads of

ability, interest, and personality. Journal of Educational Psychology, 88, 174-

188.

Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods

and data analysis (Vol. 2): McGraw-Hill New York.

Rounds, J., & Tracey, T. J. (1993). Prediger's dimensional representation of

Holland's RIASEC circumplex. Journal of Applied Psychology, 78(6), 875.

Rushton, J. P., & Irwing, P. (2008). A General Factor of Personality (GFP) from two

meta-analyses of the Big Five: and. Personality and Individual Differences,

45(7), 679-683.

151

Sak, U. (2004). A synthesis of research on psychological types of gifted adolescents.

The Journal of Secondary Gifted Education, 15(2), 70-79.

Scarr, S. (1996). How people make their own environments: Implications for parents

and policy makers. Psychology, Public Policy, and Law, 2(2), 204.

Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of work:

Occupational attainment and job performance. Journal of personality and

social psychology, 86(1), 162-173.

Sclove, L. S. (1987). Application of model-selection criteria to some problems in

multivariate analysis. Psychometrika, 52, 333-343.

Shaycroft, M. F. (1967). The high school years: Growth in cognitive skills.

Pittsburgh, PA: American Institutes for Research and School of Education,

University of Pittsburgh.

Snow, R. E. (1963). Effects of learner characteristics in learning from instructional

films. Unpublished doctoral thesis, Purdue University.

Snow, R. E. (1989). Aptitude-treatment interaction as a framework for research on

individual differences in learning. In P. L. Ackerman, R. J. Sternberg & R.

Glaser (Eds.), Learning and individual differences: Advances in theory and

research (pp. 13-59). New York: Freeman.

Soubelet, A., & Salthouse, T. A. (2011). Personality-cognition relations across

adulthood. Developmental Psychology, 47(2), 303-310.

Spearman, C. (1904). " General Intelligence," Objectively Determined and

Measured. The American Journal of Psychology, 15(2), 201-292.

Spearman, C. (1939). Thurstone's work reworked. Journal of Educational

Psychology, 30(1-16).

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.).

New York, NY: Pearson.

Thurstone, L. L. (1938). Primary mental abilities. Psychometric Monographs, No.1

Chicago: University of Chicago Press.

Thurstone, L. L. (1947). Multiple factor analysis: A development and expansion of

The Vectors of the Mind. Chicago, IL: University of Chicago Press.

Tracey, T. J., & Rounds, J. (1992). Evaluating the RIASEC circumplex using high-

point codes. Journal of Vocational Behavior, 41(3), 295-311.

Tracey, T. J., & Rounds, J. (1996). The spherical representation of vocational

interests. Journal of Vocational Behavior, 48(1), 3-41.

Tracey, T. J., & Rounds, J. B. (1993). Evaluating Holland's and Gati's vocational-

interest models: A structural meta-analysis. Psychological bulletin, 113(2),

229.

Tupes, E. C., & Christal, R. E. (1992). Recurrent personality factors based on trait

ratings. Journal of personality, 60(2), 225-251.

Undheim, J. O. (1981). On intelligence IV: Toward a restoration of general

intelligence. Scandinavian Journal of Psychology, 22, 251-265.

152

Undheim, J. O., & Gustafsson, J.-E. (1987). The hierarchical organization of

cognitive abilities: Restoring general intelligence through the use of linear

structural relations (LISREL). Multivariate Behavioral Research, 22, 149-

171.

Van Der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H.

M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence:

The positive manifold of intelligence by mutualism. Psychological Review,

113(4), 842-861.

Van Iddekinge, C. H., Putka, D. J., & Campbell, J. P. (2011). Reconsidering

vocational interests for personnel selection: The validity of an interest-based

selection test in relation to job knowledge, job performance, and continuance

intentions. Journal of Applied Psychology, 96(1), 13.

Vernon, P. E. (1961). The structure of human abilities. London: Methuen.

Vernon, P. E. (1965). Ability factors and environmental influences. American

Psychologist, 20, 723-733.

Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains:

Aligning over 50 years of cumulative psychological knowledge solidifies its

importance. Journal of Educational Psychology, 101(4), 817.

Waiyavutti, C., Johnson, W., & Deary, I. J. (2012). Do personality scale items

function differently in people with high and low IQ? Psychological

Assessment, 24(3), 545.

Wise, L. L., McLaughlin, D. H., & Steel, L. (1979). The Project TALENT data

hanbook, revised. Palo Alto, CA: American Institutes for Research.

Wolf, M. B., & Ackerman, P. L. (2005). Extraversion and intelligence: A meta-

analytic investigation. Personality and Individual Differences, 39(3), 531-

542.

Yang, C.-C. (2006). Evaluating latent class analysis models in qualitative phenotype

identification. Computational Statistics & Data Analysis, 50, 1090-1104.

Zeidner, M. (1995). Personality trait corrrelates of intelligence. In D. H. Saklofske &

M. Zeidner (Eds.), International handbook of personality and intelligence.

New York: Plenum Press.

Zeidner, M., & Shani-Zinovich, I. (2011). Do academically gifted and nongifted

students differ on the Big-Five and adaptive status? Some recent data and

conclusions. Personality and Individual Differences, 51(5), 566-570.

153

Appendix A: Supplemental tables for chapter 3

Table A1

First-order factor loadings for grade 10 males in the broad selection (VPR, CHC and Gf-Gc models)

Test Name Factor

Inform-

ation

English/

Math

Spatial/

Reasoning

Mech./

Science Speed Math

Vocabulary .64/.65/.65 .30/.31/.31

Literature .83/.84/.84

Music .73/.73/.73

Social Studies .84/.84/.84

Mathematics .32/.21/.21 .61/.68/.68

Physical Science .31/.32/.32 .47/.46/.46 .16/.15/.16

Biological Science .42/.43/.43 .36/.36/.36

Aeronautics and Space .33/.36/.36 .47/.47/.47

Electronics .82/.83/.83

Mechanics .74/.73/.73

Art .76/.76/.76

Law .68/.68/.68

Health .58/.59/.59 .05/.04/.04 .11/.13/.13

Bible .67/.67/.67

Theatre and Ballet .71/.71/.71

Miscellaneous .71/.71/.71

Memory for sentences .29/.29/.29

Memory for words .15/.14/.14 .39/.39/.39

Disguised words .33/.32/.32 .32/.34/.33 .25/.25/.26

Spelling .65/.64/.64

Capitalization .70/.69/.69

Punctuation .83/.83/.83

English usage .70/.70/.70

Effective expression .62/.62/.62

Word functions in sent. .42/.68/.67 .06/.09/.10 .33/–/–

Reading comprehension .53/.53/.54 .32/.31/.31 .10/.10/.10

Creativity .34/.36/.36 .16/.15/.15 .32/.30/.31

Mechanical reasoning .58/.57/.57 .33/.39/.39

Visualization in 2D .56/.56/.56 .23/.21/.20

Visualization in 3D .77/.78/.78

Abstract reasoning .32/.30/.30 .50/.51/.51

Math 1 .33/–/– .19/.16/.15 .37/.68/.68

Math 2 .13/–/– .78/.87/.97

Arithmetic comp. .26/–/– .34/.34/.34 .28/.50/.50

Table reading .72/.72/.73

Clerical checking .69/.69/.69

Object inspection .15/.15/.14 .59/.59/.59

Note. Loadings are in the order of VPR model, CHC model and Gf-Gc model

154

Table A2

First-order factor loadings for grade 10 females in the broad selection (VPR, CHC and Gf-Gc models)

Test Name Factor

Inform-

ation

English/

Math

Spatial/

Reasoning

Mech./

Science Speed Math

Vocabulary .58/.60/.60 .12/.13/.13 .23/.23/.23

Literature .83/.83/.83

Music .76/.76/.76

Social Studies .83/.83/.83

Mathematics .23/.12/.12 .67/.72/.72

Physical Science .66/.57/.56 .22/.31/.31

Biological Science .28/.35/.35 .45/.40/.40

Aeronautics and Space .16/.21/.21 .38/.35/.35

Electronics .59/.62/.62

Mechanics .17/.20/.20 .51/.50/.51

Art .77/.77/.77

Law .61/.61/.61

Health .47/.50/.50 .13/.10/.10 .11/.11/.11

Bible .62/.63/.63

Theatre and Ballet .73/.73/.73

Miscellaneous .67/.67/.67

Memory for sentences .39/.38/.38

Memory for words .20/.17/.17 .41/.44/.44

Disguised words .33/.31/.31 .37/.38/.38 .27/.27/.27

Spelling .66/.65/.66

Capitalization .70/.69/.69

Punctuation .85/.84/.85

English usage .69/.69/.69

Effective expression .09/.07/.07 .52/.53/.53

Word functions in sent. .43/.67/.66 .11/.14/.15 .33/–/–

Reading comprehension .53/.53/.53 .31/.30/.30 .11/.12/.12

Creativity .42/.42/.41 .34/.35/.35

Mechanical reasoning .64/.64/.64 .19/.19/.20

Visualization in 2D .59/.58/.58 .20/.20/.18

Visualization in 3D .75/.74/74

Abstract reasoning .34/.32/.32 .49/.50/.50

Math 1 .36/–/– .21/.20/.20 .31/.63/.63

Math 2 .21/–/– .69/.84/.84

Arithmetic comp. .44/–/– .29/.30/.31 .18/.57/.57

Table reading .25/.24/.23 .67/.67/.68

Clerical checking .69/.69/.68

Object inspection .24/.24/.22 .63/.63/.62

Note. Loadings are in the order of VPR model, CHC model and Gf-Gc model

155

Appendix B: Supplemental tables for chapter 4

Table B1

Standardized linear and quadratic effects of g on the raw personality scales (males)

Trait Linear effect Quadratic effect

Gr. 9 Gr. 10 Gr. 11 Gr.12 Gr. 9 Gr. 10 Gr. 11 Gr. 12

Sociability

Beta .155 .108 .066 .049 -.119 -.118 -.124 -.132

R2 .024 .012 .004 .002 .021 .020 .023 .025

Calmness

Beta .264 .261 .248 .246 – – – –

R2 .070 .068 .062 .061 – – – –

Vigor

Beta .233 .196 .165 .146 -.094 -.091 -.084 -.090

R2 .054 .038 .027 .021 .012 .011 .009 .011

Social Sensitivity

Beta .202 .202 .197 .193 – – -.018 -.024

R2 .041 .041 .039 .037 – – .000 .001

Tidiness

Beta .212 .188 .142 .114 -.058 -.052 -.061 -.068

R2 .045 .035 .020 .013 .004 .004 .005 .006

Culture

Beta .161 .156 .130 .132 – .023 .040 .048

R2 .026 .024 .017 .017 – .001 .003 .004

Self-Confidence

Beta .245 .218 .212 .222 – – – .017

R2 .060 .047 .045 .049 – – – .001

Mature Personality

Beta .273 .255 .241 .230 .044 .051 .054 .044

R2 .075 .065 .058 .053 .003 .005 .006 .004

Impulsiveness

Beta – .032 .030 .044 – – – –

R2 – .001 .001 .002 – – – –

Leadership

Beta .060 .071 .093 .116 .084 .080 .073 .065

R2 .004 .005 .009 .013 .010 .010 .008 .007

Effects greater than .015 are significant at p < .001, with no adjustment for multiple testing. Non-significant

effects are not shown.

156

Table B2

Standardized linear and quadratic effects of g on the personality scales (females)

Trait Linear effect Quadratic effect

Gr. 9 Gr. 10 Gr. 11 Gr.12 Gr. 9 Gr. 10 Gr. 11 Gr. 12

Sociability

Beta .129 .075 .028 -.023 -.138 -.149 -.148 -.138

R2 .017 .006 .001 .000 .030 .033 .033 .030

Calmness

Beta .226 .207 .201 .175 -.028 -.033 -.028 -.037

R2 .051 .043 .040 .031 .001 .001 .001 .002

Vigor

Beta .221 .177 .146 .118 -.078 -.088 -.077 -.067

R2 .049 .031 .021 .014 .008 .011 .009 .006

Social Sensitivity

Beta .237 .217 .209 .175 -.060 -.084 -.086 -.086

R2 .056 .047 .044 .031 .006 .011 .011 .011

Tidiness

Beta .176 .121 .086 .032 -.085 -.099 -.103 -.116

R2 .031 .015 .007 .001 .011 .014 .016 .020

Culture

Beta .254 .252 .247 .226 -.014 -.016 -.011 -.004

R2 .065 .063 .061 .051 .000 .000 .000 .000

Self-Confidence

Beta .185 .165 .164 .169 -.007 -.003 .011 .013

R2 .034 .027 .027 .029 .000 .000 .000 .000

Mature Personality

Beta .282 .264 .288 .269 .059 .055 .050 .036

R2 .080 .069 .083 .072 .005 .006 .004 .003

Impulsiveness

Beta .071 .119 .113 .117 .040 .046 .021 .023

R2 .005 .014 .013 .014 .002 .003 .001 .001

Leadership

Beta .074 .057 .072 .095 .052 .047 .050 .056

R2 .005 .003 .005 .009 .004 .004 .004 .005

Effects greater than .015 are significant at p < .001, with no adjustment for multiple testing. Non-significant

effects are not shown.

157

Table B3 AIC differences between null and full GAM models (grade 10 males/females) Trait AIC difference - males AIC difference - females Sociability 1451.7 1927.4 Calmness 371.5 13.2 Vigor 841.1 406.6 Social sensitivity 16.1 303.0 Tidiness 258.2 969.0 Culture 522.8 275.2 Self-confidence 502.8 116.8 Mature personality 805.8 1781.1 Impulsiveness 268.3 456.5 Leadership 1145.0 1132.3 Note: the AIC for null model in males was 134924.2, and 135009.3 in females.

158

Appendix C: Supplemental tables for chapter 5

Table C1. Odds ratios of abilities and interests predicting job categories (grade 11 males).

Predictor Job Category

Science Med. Business Teach. Humanities Fine Arts Technical Sales Mech. Clerical Construc. Labour

g 2.64 2.97 1.38 1.34 3.50 1.32 1.33 .56 .66 .50

Spatial 1.38 1.35 .73 1.33 1.26

English 1.43 1.19 .79 1.25

Speed 1.23 .77 1.24

Math 1.39 1.33 .84 1.17

I: Trades .73 .70 .81 .78 .46 1.65 1.49

I: Politics 1.29 .72 1.43 .78

I: Science 1.60 1.56 .78 .81 1.31 1.15 1.50

I: Clerical 1.15 1.16 .82 .65 1.27 .82

I: Medicine 2.27 .82 .77

I: Arts .75 1.21 .82 1.55 1.44 .70 1.17 .81 1.18

I: Teaching 1.74 1.35 .76 .70 1.27

I: Military 1.25 .77 1.19

I: Sales .61 .77 1.18 1.66 1.71 .68 .78 1.54

I: Arch. 1.54 .57 1.22 .79 1.29 1.28 .82

F1: Trades .17 .11 .55 .70 .08 .55 .81 3.05 1.71 3.71 2.91

F2: People .23 1.16 2.66 2.55 .58 .56 .48 1.70 .76

F3: Things 19.58 5.67 1.56 .61 2.62 3.70 2.31 .82 .80 .40 .53 .34

R2: full .38 .49 .10 .20 .51 .20 .09 .03 .24 .13 .23 .02

R2: factors .53 .51 .09 .18 .61 .17 .09 .01 .28 .10 .27 .21

Note: Odds ratios between .86 and 1.14 not shown. R2: full was for individual scales as predictors; R

2: factors was for the trait-complex factors.

Med. = Medicine, Mech. = Mechanical.

159

Table C2. Odds ratios of abilities and interests predicting job categories (grade 11 females).

Predictor Job Category

Science Med. Business Teaching Humanities Fine

Arts

Technical Sales Mech. Clerical Construc. Labour

g 2.51 1.96 1.83 1.33 1.82 .64 .80 .32 .28 .52

Spatial 1.40 .72 .79 .83 1.41 1.33 1.90 .39

English 1.70 .67 .83 .80 1.34 .80 1.40 .40

Speed .84 1.33 .56 1.63 .56

Math .79 .78 1.17 1.34

I: Trades .34 1.26 .82 .85 .55 1.61 1.27

I: Politics 1.42 1.20 1.37 1.21 .82 1.49 .62 .65

I: Science 2.47 1.24 .84 .58 1.39 1.77

I: Clerical 2.47 1.33 .49 .69 .47 1.25 1.41 .43

I: Medicine 2.75 3.23 .76 .82 1.90 1.15 .79 1.62

I: Arts .82 1.37 1.53 1.15 1.23 1.25 1.88 1.50

I: Teaching 1.67 .81 1.70 1.25 2.35 .82 .56 .34 .81

I: Military 1.44 .75 16.63 1.33

I: Sales .34 .64 1.15 .65 1.33 .66 1.66 .84

F1: Clerical .53 .10 .58 .29 .32 .46 1.18 2.56 1.52 1.26 1.90

F2: People 1.24 2.66 5.20 2.59 2.97 .85 1.22 .27 .68 .44 .40

F3: Things 1.74 5.67 .51 .52 .63 .83 1.56 1.25 4.06 1.77

R2: full .53 .36 .19 .25 .26 .30 .19 .13 .45 .06 .80 .18

R2: factors .18 .51 .11 .32 .29 .16 .01 .02 .24 .04 .22 .11

Note: Odds ratios between .86 and 1.14 not shown. R2: full was for individual scales as predictors; R

2: factors was for the trait-complex factors.

Med. = Medicine, Mech. = Mechanical.

160

Table C3. Odds ratios of latent classes in predicting job category (grade 11 males).

Predictor Job Category

Science Med. Business Teaching Humanities Fine

Arts

Technical Sales Mech. Clerical Construc. Labour

Class 1 .56 .09 .47 .79 .68 1.17 1.95 1.14 1.92

Class 2 .83 .41 .70 .22 .79 1.17 1.51 .80

Class 3 .15 .76 3.24 3.66 1.26 .18 .80 2.03 .32 1.43

Class 5 .60 .37 1.25 2.29 2.21 1.18 1.21 .62 1.24 1.47 .75

R2 .10 .13 .03 .06 .22 .01 .01 .10 .02 .02 .07 .01

Note: Reference class is class 4. Odds ratios between .86 and 1.14 not shown. Med. = Medicine, Mech. = Mechanical

Table C4. Odds ratios of latent classes in predicting job category (grade 11 females).

Predictor Job Category

Science Med. Business Teaching Humanities Fine

Arts

Technical Sales Mech. Clerical Construc. Labour

Class 1 .11 .35 .67 .73 .53 1.53 n/a 1.21

Class 3 1.70 .73 2.11 1.89 1.52 1.27 1.30 .51 1.20 n/a .73

Class 4 .58 2.60 1.24 2.20 5.29 1.57 1.29 1.48 .19 .67 n/a .54

Class 5 .26 1.33 .42 2.67 1.24 .52 .60 .73 1.27 n/a 1.35

Class 6 .72 3.00 .85 2.00 .70 2.90 .32 1.49 .24 n/a 1.64

R2 .23 .12 .04 .05 .15 .02 .04 .02 .09 .01 n/a .03

Note: Reference class is class 2. Odds ratios between .86 and 1.14 not shown. Med. = Medicine, Mech. = Mechanical

a model did not converge.